In the file each article is seperated by 'NEW_PAPER' in 'covid-scienc.txt' and 'NEW NEWSPAPER' in 'covid-non-science'.
#import
import os.path
from gensim import corpora
from gensim.models import LsiModel
from nltk.tokenize import RegexpTokenizer
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
from gensim.models.coherencemodel import CoherenceModel
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import sklearn as s
import dataframe_image as dfi
import networkx as nx
C:\ProgramData\Anaconda3\lib\site-packages\gensim\similarities\__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning. warnings.warn(msg)
def load_data(path,file_name,seperator):
"""
Input : path and file_name
Purpose: loading text file
Output : list of paragraphs/documents and
title(initial 100 words considred as title of document)
"""
documents_list = []
titles=[]
document = ''
i=0
with open( os.path.join(path, file_name) ,"r", encoding="utf8") as fin:
data = fin.read()
words = data.split()
fin.seek(0)
print('Number of words in text file :', len(words))
for line in fin.readlines():
text = line.strip()
i=i+1
if text !='':
document = document+text
if text == seperator:
documents_list.append(document)
document = ''
documents_list.append(document)
print("Total Number of Documents:",len(documents_list))
titles.append( text[0:min(len(text),100)] )
return documents_list,titles,len(words)
document_list,titles,word_count=load_data("","covid-science-large.txt",'NEW_PAPER')
Number of words in text file : 274941 Total Number of Documents: 40
def preprocess_data(doc_set):
"""
Input : docuemnt list
Purpose: preprocess text (tokenize, removing stopwords, and stemming)
Output : preprocessed text
"""
# initialize regex tokenizer
tokenizer = RegexpTokenizer(r'\w+')
# create English stop words list
en_stop = set(stopwords.words('english'))
# Create p_stemmer of class PorterStemmer
p_stemmer = PorterStemmer()
# list for tokenized documents in loop
texts = []
# loop through document list
for i in doc_set:
# clean and tokenize document string
raw = i.lower()
tokens = tokenizer.tokenize(raw)
tokens = [i for i in tokens if len(i)>4]
print(tokens)
# remove stop words from tokens
stopped_tokens = [i for i in tokens if not i in en_stop]
# stem tokens
stemmed_tokens = [p_stemmer.stem(i) for i in stopped_tokens]
# add tokens to list
texts.append(stemmed_tokens)
return texts
clean_text=preprocess_data(document_list)
['covidgr', 'dataset', 'covid', 'sdnetmethodology', 'predicting', 'covid', '19based', 'chest', 'imagess', 'tabik', 'gómez', 'martín', 'rodríguez', 'sevillano', 'garcía', 'charte', 'guirado', 'suárez', 'luengo', 'valero', 'gonzález', 'garcía', 'villanova', 'olmedo', 'sánchez', 'herreraabstract', 'currently', 'coronavirus', 'disease', 'covid', 'infectious', 'diseases', 'century', 'diagnosed', 'using', 'testing', 'scans', 'orchest', 'images', 'computed', 'tomography', 'scanners', 'testing', 'available', 'mostmedical', 'centers', 'hence', 'cases', 'imagesbecome', 'effective', 'assisting', 'clinicians', 'making', 'decisions', 'learning', 'neural', 'networkshave', 'great', 'potential', 'building', 'covid', 'triage', 'systemsand', 'detecting', 'covid', 'patients', 'especially', 'patients', 'withlow', 'severity', 'unfortunately', 'current', 'databases', 'allowbuilding', 'systems', 'highly', 'heterogeneousand', 'biased', 'towards', 'severe', 'cases', 'article', 'threefold', 'demystify', 'sensitivities', 'achieved', 'bymost', 'recent', 'covid', 'classification', 'models', 'under', 'aclose', 'collaboration', 'hospital', 'universitario', 'clínico', 'sancecilio', 'granada', 'spain', 'built', 'covidgr', 'homogeneous', 'balanced', 'database', 'includes', 'levelsmanuscript', 'received', 'september', 'revised', 'october', 'accepted', 'november', 'publication', 'november', 'current', 'version', 'december', 'supported', 'bythe', 'project', 'deepscop', 'ayudas', 'fundación', 'equipos', 'investigación', 'científica', 'covid19_rx', 'ayudas', 'fundaciónbbva', 'equipos', 'investigación', 'científica', 'covid', '192020', 'spanish', 'ministry', 'science', 'technology', 'under', 'theproject', 'tin2017', '89517', 'tabik', 'supported', 'ramon', 'cajalprogramme', '18136', 'gómez', 'supported', 'thefpu', 'programme', 'fpu16', '04765', 'charte', 'supported', 'fpuprogramme', 'fpu17', '04069', 'suárez', 'supported', 'programme', 'fpu18', '05989', 'supported', 'european', 'researchcouncil', 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'medicaldoctor', 'their', 'generous', 'feedbacks', 'continuousyamaç', 'convolutional', 'sparse', 'support', 'estimator', 'based', 'covid', 'recognition', '1819proof', 'reading', 'khalid', 'hameed', 'medicalcenter', 'qatar', 'tahir', 'hamid', 'consultant', 'cardiologist', 'inhamad', 'medical', 'corporation', 'hospital', 'weill', 'cornellmedicine', 'qatar', 'rashid', 'mazhar', 'hamadmedical', 'corporation', 'hospital', 'qatar']
def prepare_corpus(doc_clean):
"""
Input : clean document
Purpose: create term dictionary of our courpus and Converting list of documents (corpus) into Document Term Matrix
Output : term dictionary and Document Term Matrix
"""
# Creating the term dictionary of our courpus, where every unique term is assigned an index. dictionary = corpora.Dictionary(doc_clean)
dictionary = corpora.Dictionary(doc_clean)
# Converting list of documents (corpus) into Document Term Matrix using dictionary prepared above.
doc_term_matrix = [dictionary.doc2bow(doc) for doc in doc_clean]
# generate LDA model
return dictionary,doc_term_matrix
def create_gensim_lsa_model(doc_clean,number_of_topics,words):
"""
Input : clean document, number of topics and number of words associated with each topic
Purpose: create LSA model using gensim
Output : return LSA model
"""
dictionary,doc_term_matrix=prepare_corpus(doc_clean)
# generate LSA model
lsamodel = LsiModel(doc_term_matrix, num_topics=number_of_topics, id2word = dictionary) # train model
#print(lsamodel.print_topics(num_topics=number_of_topics, num_words=words))
return lsamodel
# LSA Model
number_of_topics=1
words=word_count
model=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_science =dict(model.show_topic(0, topn=words))##dict and encoding matrix values
dict(model.show_topic(0, topn=5))
{'covid': 0.5580672321131054,
'imag': 0.2828570178358241,
'model': 0.257499828486703,
'dataset': 0.142562489056061,
'infect': 0.13935041563870323}
from wordcloud import WordCloud
text = dict(model.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph21.png')
# LSA Model
number_of_topics = 25 #
words = word_count
model = create_gensim_lsa_model(clean_text,number_of_topics,words)
a=pd.DataFrame()
for i in range(number_of_topics):
words_science =dict(model.show_topic(i, topn=words))
b=pd.DataFrame(words_science,index=[i])
a=a.append(b)
a=a.transpose()
top_words = dict(model.show_topic(0,topn=5))
df = pd.DataFrame(columns=list(top_words.keys()),
index = list(top_words.keys()))
l = list(top_words.keys())
print('cosine-similarity')
for i in l:
for j in l:
matrix=np.array([a.loc[i],a.loc[j]])
n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
df.loc[[i],[j]] = n[0,1]
print(df)
dfi.export(df, 'df_styled24.png')
cosine-similarity
covid imag model dataset infect
covid 1 0.0792291 0.0523905 -0.0570346 0.0548166
imag 0.0792291 1 -0.0709453 0.184574 -0.0203597
model 0.0523905 -0.0709453 1 0.0116004 0.103497
dataset -0.0570346 0.184574 0.0116004 1 -0.181223
infect 0.0548166 -0.0203597 0.103497 -0.181223 1
df = df.apply(pd.to_numeric, errors='coerce')
df=df.round(4)
l = list(top_words.keys())
df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.5)}
a=list(G.edges(data=True))
b=[]
for i in a:
b.append(i[:][2]['weight'])
w = [x *10 for x in b]
colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='green',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig24.png')
document_list,titles,word_count=load_data("","covid-psychology-large.txt",'NEW PAPER')
Number of words in text file : 183592 Total Number of Documents: 40
clean_text=preprocess_data(document_list)
['international', 'sociology2021', 'author', '2020article', 'reuse', 'guidelines', 'sagepub', 'journals', 'permissionsdoi', '0268580920948807journals', 'sagepub', 'isspsychology', 'politics', 'ofcovid', 'misinfodemics', 'peoplebelieve', 'misinfodemics', 'sonia', 'mukhtaruniversity', 'management', 'technology', 'lahore', 'pakistanabstractmisinfodemics', 'related', 'covid', 'negatively', 'impacted', 'people', 'lives', 'adversehealth', 'psycho', 'sociopolitical', 'outcomes', 'scientific', 'community', 'seeks', 'communicateevidence', 'based', 'information', 'regarding', 'misplaced', 'preventive', 'strategies', 'misinformed', 'helpseeking', 'behaviors', 'global', 'multifaceted', 'systems', 'secondary', 'emerged', 'effects', 'ofmisinfodemics', 'public', 'published', 'articles', 'pubmed', 'embase', 'google', 'scholar', 'elsevierabout', 'covid', 'related', 'misinfodemics', 'considered', 'reviewed', 'article', 'thisreview', 'examines', 'mechanisms', 'operational', 'structure', 'prevalence', 'predictive', 'factors', 'effects', 'responses', 'potential', 'curtailing', 'strategies', 'misinfodemics', 'covid', 'present', 'articleshows', 'popular', 'variants', 'covid', 'misinfodemics', 'could', 'joint', 'product', 'apsychological', 'predisposition', 'which', 'either', 'reject', 'information', 'experts', 'perceive', 'thecrisis', 'situation', 'product', 'misinfodemics', 'mechanisms', 'partisan', 'ideological', 'motivations', 'psychological', 'foundations', 'political', 'disposition', 'misinfodemics', 'implications', 'thedevelopment', 'strategies', 'designed', 'curtail', 'negative', 'consequences', 'public', 'health', 'keywordsconspiracy', 'theories', 'covid', 'health', 'communication', 'misinformation', 'mistrust', 'sciencethe', 'mechanisms', 'misinfodemicsin', 'modern', 'mainstream', 'social', 'media', 'become', 'primary', 'source', 'information', 'people', 'around', 'world', 'misinfodemics', 'surroundingcorresponding', 'author', 'sonia', 'mukhtar', 'university', 'management', 'technology', 'block', 'phase', 'johar', 'lahore', 'punjab', '54770', 'pakistan', 'email', 'sonia', 'mukhtar12', 'gmail', 'com948807iss0010', '0268580920948807international', 'sociologymukhtarresearch', 'article2020article112', 'international', 'sociology', 'covid', 'pandemic', 'challenging', 'ongoing', 'coronavirus', 'covid', 'pandemic', 'outbreak', 'highlighted', 'interconnectedness', 'modernglobalized', 'world', 'where', 'public', 'health', 'threats', 'extend', 'beyond', 'their', 'point', 'origindue', 'unvarying', 'reliance', 'misplaced', 'confidence', 'media', 'platforms', 'duringlockdown', 'quarantine', 'isolation', 'social', 'distancing', 'virtual', 'communication', 'hasbecome', 'major', 'source', 'interaction', 'holman', 'covid', 'pandemic', 'outbreak', 'escalated', 'challenges', 'forhealthcare', 'social', 'educational', 'economic', 'political', 'environmental', 'cultural', 'socioeconomic', 'systems', 'world', 'gained', 'momentum', 'innumerablemisinfodemics', 'mechanisms', 'rumors', 'myths', 'superstitions', 'conspiracy', 'theories', 'claims', 'hoaxes', 'false', 'misinformation', 'polarization', 'mistrust', 'science', 'intimes', 'crisis', 'absence', 'checking', 'misinformation', 'misleading', 'content', 'falsecontext', 'manipulated', 'fabricated', 'material', 'imposter', 'documents', 'disinformation', 'regarding', 'etiology', 'outcomes', 'misplaced', 'prevention', 'disease', 'mukhtar', '2020a', 'covid', 'pandemic', 'outbreak', 'saturated', 'mainstream', 'media', 'which', 'disseminate', 'information', 'local', 'global', 'scale', 'similarly', 'social', 'media', 'platforms', 'havealso', 'become', 'accessible', 'source', 'information', 'numerous', 'incidents', 'initiated', 'bythese', 'rumors', 'caused', 'several', 'mishaps', 'across', 'world', 'instance', 'hisown', 'after', 'positive', 'diagnosis', 'covid', 'india', 'people', 'overdosed', 'onthe', 'cholorquine', 'after', 'about', 'effectiveness', 'against', 'covid', 'proliferated', 'nigeria', 'busari', 'adebayo', 'there', 'failure', 'makea', 'distinction', 'between', 'empirically', 'based', 'scientific', 'truths', 'fabricated', 'unconfirmedanti', 'science', 'conspiracy', 'theories', 'among', 'general', 'public', 'reports', 'reduce', 'legitimacy', 'scientific', 'discoveries', 'regarding', 'vaccine', 'covid', 'canalso', 'create', 'social', 'stigma', 'resulting', 'xenophobia', 'chinese', 'sentiment', 'racism', 'marginalization', 'reduced', 'compliance', 'adherence', 'quarantine', 'adverse', 'healthand', 'psychosocial', 'impacts', 'aguilera', '2020a', '2020b', 'these', 'considerations', 'become', 'exacerbated', 'during', 'lockdown', 'leading', 'people', 'fringe', 'ofpopular', 'opinion', 'spend', 'social', 'media', 'people', 'trying', 'tomake', 'sense', 'their', 'changed', 'lives', 'proclaimed', 'celebrities', 'identified', 'socialmedia', 'stars', 'politicians', 'mainstream', 'media', 'public', 'figures', 'propagating', 'theirown', 'subjective', 'interpretations', 'events', 'situation', 'covid', 'pandemic', 'misinfodemics', 'lederer', 'scientific', 'studies', 'previous', 'disease', 'outbreaks', 'demonstrated', 'misinformation', 'represents', 'secondary', 'challenge', 'public', 'health', 'efforts', 'controlling', 'epidemic', 'pandemic', 'earnshaw', 'kalichman', 'individuals', 'endorsemisinformation', 'about', 'disease', 'likely', 'follow', 'public', 'health', 'instructions', 'duringthe', 'ebola', 'outbreak', 'respondents', 'credence', 'conspiracy', 'theoriesalleged', 'would', 'likely', 'support', 'during', 'disease', 'outbreak', 'national', 'cancer', 'institute', 'study', 'montanaro', 'conducted', 'theus', 'respondents', 'reported', 'trust', 'medical', 'healthcare', 'professionals', 'contrast', 'study', 'where', 'majority', 'respondents', 'reported', 'mistrust', 'informationabout', 'covid', 'current', 'administration', 'mainstream', 'media', 'outlets', 'times', 'mukhtar', '113the', 'psychology', 'misinfodemicsfear', 'adaptive', 'emotion', 'which', 'serves', 'mobilize', 'energy', 'towards', 'potential', 'actualor', 'perceived', 'threat', 'mertens', 'however', 'excessive', 'thismay', 'detrimental', 'effects', 'individual', 'mental', 'health', 'problems', 'anxiety', 'andphobia', 'community', 'level', 'hoarding', 'panic', 'shopping', 'xenophobia', 'isinsufficient', 'cause', 'individual', 'incompliance', 'nonadherence', 'towards', 'quarantine', 'isolation', 'community', 'reckless', 'implementation', 'ofpolicies', 'ignores', 'risks', 'socioeconomic', 'infrastructure', 'likewise', 'interpersonalsafety', 'measures', 'mitigate', 'certain', 'threats', 'transmission', 'disease', 'paradoxically', 'enhance', 'transmission', 'excessive', 'health', 'anxiety', 'psychosocialissues', 'similarly', 'social', 'safety', 'measures', 'lockdowns', 'curfews', 'control', 'transmission', 'prolonged', 'stringent', 'measures', 'could', 'negative', 'consequences', 'socioeconomic', 'impact', 'mental', 'health', 'impact', 'efforts', 'misinfodemics', 'process', 'increased', 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'volume', 'article', '625756testoni', 'psychology', 'students', 'perceptions', 'covid', 'covid', 'pandemic', 'could', 'indeed', 'young', 'adultsobtain', 'proper', 'resources', 'personal', 'skills', 'toadequately', 'period', 'intense', 'uncertainty', 'mortalitysalience', 'other', 'future', 'challenge', 'there', 'might', 'store', 'especially', 'healthcare', 'students', 'psychologists', 'example', 'properly', 'address', 'these', 'themes', 'withtheir', 'patients', 'future', 'proper', 'death', 'education', 'coursesshould', 'therefore', 'implemented', 'keeping', 'toallow', 'students', 'openly', 'discuss', 'without', 'prejudices', 'thetheme', 'death', 'human', 'vulnerability', 'express', 'relatedemotions', 'through', 'could', 'helpful', 'limits', 'future', 'perspectivesthe', 'research', 'reports', 'encouraging', 'results', 'demonstrate', 'thepositive', 'effects', 'death', 'education', 'however', 'limitationsshould', 'taken', 'account', 'including', 'inability', 'generalizethe', 'results', 'given', 'specificity', 'conditions', 'concerning', 'future', 'research', 'should', 'focus', 'significantly', 'broaden', 'numberof', 'participants', 'addition', 'longitudinalstudy', 'considered', 'approach', 'discussionof', 'existential', 'issues', 'addressed', 'actually', 'changed', 'andkept', 'stable', 'moreover', 'since', 'research', 'participantswere', 'mostly', 'female', 'though', 'psychology', 'students', 'areusually', 'female', 'future', 'research', 'could', 'exploremore', 'precisely', 'psychology', 'students', 'point', 'comparing', 'female', 'students', 'other', 'interestingaspects', 'could', 'investigated', 'future', 'studies', 'could', 'bethe', 'perspective', 'medical', 'nursing', 'social', 'services', 'students', 'being', 'conducted', 'other', 'cultural', 'contexts', 'sincecomparing', 'psychology', 'students', 'perspectives', 'point', 'viewof', 'other', 'healthcare', 'social', 'services', 'professionals', 'could', 'beparticularly', 'interesting', 'informative', 'availability', 'statementthe', 'supporting', 'conclusions', 'thisarticle', 'available', 'authors', 'withoutundue', 'reservation', 'ethics', 'statementthe', 'studies', 'involving', 'human', 'participants', 'werereviewed', 'approved', 'ethical', 'committee', 'forthe', 'psychological', 'research', 'university', 'ofpadua', '57bc2002fdf5cbd4292f5f86aa077f23', 'thepatients', 'participants', 'provided', 'their', 'written', 'informed', 'consent', 'toparticipate', 'study', 'author', 'contributionsit', 'contributed', 'research', 'design', 'project', 'planning', 'supervision', 'research', 'analysis', 'texts', 'methodology', 'article', 'writing', 'contributed', 'project', 'planning', 'analysis', 'texts', 'article', 'writing', 'contributed', 'tothe', 'interviews', 'analysis', 'texts', 'contributed', 'thesupervision', 'cooperation', 'organization', 'teamwork', 'andwriting', 'contributed', 'analysis', 'texts', 'cooperation', 'organization', 'contributed', 'tothe', 'writing', 'authors', 'contributed', 'article', 'approvedthe', 'submitted', 'version', 'acknowledgmentswe', 'thank', 'davide', 'viezzoli', 'marco', 'antonellini', 'alice', 'culcasi', 'andgianmarco', 'biancalani', 'critical', 'phases', 'ofthe']
# LSA Model
number_of_topics=1
words=word_count
model=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_psychology =dict(model.show_topic(0, topn=words))#dict and encoding matrix values
dict(model.show_topic(0,topn=5))
{'student': 0.3152097530189239,
'psycholog': 0.308822521032326,
'covid': 0.3082634599864981,
'train': 0.2233056339953604,
'pandem': 0.21884380058036124}
from wordcloud import WordCloud
text = dict(model.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph22.png')
# LSA Model
number_of_topics = 25 #
words = word_count
model = create_gensim_lsa_model(clean_text,number_of_topics,words)
a=pd.DataFrame()
for i in range(number_of_topics):
words_psy =dict(model.show_topic(i, topn=words))
b=pd.DataFrame(words_psy,index=[i])
a=a.append(b)
a=a.transpose()
top_words = dict(model.show_topic(0,topn=5))
df = pd.DataFrame(columns=list(top_words.keys()),
index = list(top_words.keys()))
l = list(top_words.keys())
print('cosine-similarity')
for i in l:
for j in l:
matrix=np.array([a.loc[i],a.loc[j]])
n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
df.loc[[i],[j]] = n[0,1]
print(df)
dfi.export(df, 'df_styled25.png')
cosine-similarity
student psycholog covid train pandem
student 1 0.0579216 0.0411114 0.105376 0.064141
psycholog 0.0579216 1 0.157892 0.152138 0.166166
covid 0.0411114 0.157892 1 -0.0037664 0.284789
train 0.105376 0.152138 -0.0037664 1 0.227891
pandem 0.064141 0.166166 0.284789 0.227891 1
df = df.apply(pd.to_numeric, errors='coerce')
df=df.round(4)
l = list(top_words.keys())
df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.5)}
a=list(G.edges(data=True))
b=[]
for i in a:
b.append(i[:][2]['weight'])
w = [x *10 for x in b]
colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='r',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig25.png')
def load_data(path,file_name):
"""
Input : path and file_name
Purpose: loading text file
Output : list of paragraphs/documents and
title(initial 100 words considred as title of document)
"""
documents_list = []
titles=[]
document = ''
i=0
with open( os.path.join(path, file_name) ,"r",encoding="utf8") as fin:
data = fin.read()
words = data.split()
fin.seek(0)
print('Number of words in text file :', len(words))
for word in words:
i=i+1
document = document+word+' '
if i == 200:
documents_list.append(document)
document,i = '',0
if len(documents_list) == 2:
break
print("Total Number of Documents:",len(documents_list))
#titles.append( text[0:min(len(text),100)] )
return documents_list,titles,len(words)
document_list,titles,word_count=load_data("","covid-science-large.txt")
Number of words in text file : 274941 Total Number of Documents: 2
clean_text=preprocess_data(document_list)
['covidgr', 'dataset', 'covid', 'sdnet', 'methodology', 'predicting', 'covid', 'based', 'chest', 'images', 'tabik', 'gómez', 'martín', 'rodríguez', 'sevillano', 'garcía', 'charte', 'guirado', 'suárez', 'luengo', 'valero', 'gonzález', 'garcía', 'villanova', 'olmedo', 'sánchez', 'herrera', 'abstract', 'currently', 'coronavirus', 'disease', 'covid', 'infectious', 'diseases', 'century', 'diagnosed', 'using', 'testing', 'scans', 'chest', 'images', 'computed', 'tomography', 'scanners', 'testing', 'available', 'medical', 'centers', 'hence', 'cases', 'images', 'become', 'effective', 'assisting', 'clinicians', 'making', 'decisions', 'learning', 'neural', 'networks', 'great', 'potential', 'building', 'covid', 'triage', 'systems', 'detecting', 'covid', 'patients', 'especially', 'patients', 'severity', 'unfortunately', 'current', 'databases', 'allow', 'building', 'systems', 'highly', 'heterogeneous', 'biased', 'towards', 'severe', 'cases', 'article', 'threefold', 'demystify', 'sensitivities', 'achieved', 'recent', 'covid', 'classification', 'models', 'under', 'close', 'collaboration', 'hospital', 'universitario', 'clínico', 'cecilio', 'granada', 'spain', 'built', 'covidgr', 'homogeneous', 'balanced', 'database', 'includes', 'levels', 'manuscript', 'received', 'september', 'revised', 'october', 'accepted', 'november'] ['publication', 'november', 'current', 'version', 'december', 'supported', 'project', 'deepscop', 'ayudas', 'fundación', 'equipos', 'investigación', 'científica', 'covid19_rx', 'ayudas', 'fundación', 'equipos', 'investigación', 'científica', 'covid', 'spanish', 'ministry', 'science', 'technology', 'under', 'project', 'tin2017', '89517', 'tabik', 'supported', 'ramon', 'cajal', 'programme', '18136', 'gómez', 'supported', 'programme', 'fpu16', '04765', 'charte', 'supported', 'programme', 'fpu17', '04069', 'suárez', 'supported', 'programme', 'fpu18', '05989', 'supported', 'european', 'research', 'council', 'grant', 'agreement', '647038', 'biodesert', 'project', 'approved', 'provincial', 'research', 'ethics', 'committee', 'granada', 'corresponding', 'author', 'siham', 'tabik', 'tabik', 'gómez', 'sevillano', 'garcía', 'charte', 'suárez', 'luengo', 'herrera', 'andalusian', 'research', 'institute', 'science', 'computational', 'intelligence', 'university', 'granada', '18071', 'granada', 'spain', 'siham', 'anabelgrios', 'decsai', 'isega24ivan', 'gmail', 'fdavidcl', 'jlsuarezdiaz', 'julianlm', 'decsai', 'herrera', 'decsai', 'martín', 'rodríguez', 'valero', 'gonzález', 'garcía', 'villanova', 'olmedo', 'sánchez', 'hospital', 'universitario', 'clínico', 'cecilio', 'granada', '36310', 'spain', 'joseluismartin', 'hotmail', 'valerogonzalez', 'yahoo', 'pgvillanova', 'gmail', 'euolm', 'yahoo', 'atlanttic', 'research', 'center']
# LSA Model
number_of_topics=1
words=word_count
model=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_science =dict(model.show_topic(0, topn=words))##dict and encoding matrix values
dict(model.show_topic(0, topn=5))
{'support': -0.32899471788278933,
'granada': -0.24814037209037257,
'covid': -0.2276958139915433,
'programm': -0.2193298119218597,
'research': -0.2193298119218597}
from wordcloud import WordCloud
text = dict(model.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph23.png')
# LSA Model
number_of_topics = 2 #
words = word_count
model = create_gensim_lsa_model(clean_text,number_of_topics,words)
a=pd.DataFrame()
for i in range(number_of_topics):
words_science =dict(model.show_topic(i, topn=words))
b=pd.DataFrame(words_science,index=[i])
a=a.append(b)
a=a.transpose()
top_words = dict(model.show_topic(0,topn=5))
df = pd.DataFrame(columns=list(top_words.keys()),
index = list(top_words.keys()))
l = list(top_words.keys())
print('cosine-similarity')
for i in l:
for j in l:
matrix=np.array([a.loc[i],a.loc[j]])
n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
df.loc[[i],[j]] = n[0,1]
print(df)
dfi.export(df, 'df_styled26.png')
cosine-similarity
support granada covid research programm
support 1 0.956969 -0.0794645 1 1
granada 0.956969 1 0.213226 0.956969 0.956969
covid -0.0794645 0.213226 1 -0.0794645 -0.0794645
research 1 0.956969 -0.0794645 1 1
programm 1 0.956969 -0.0794645 1 1
df = df.apply(pd.to_numeric, errors='coerce')
df=df.round(4)
l = list(top_words.keys())
df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.5)}
a=list(G.edges(data=True))
b=[]
for i in a:
b.append(i[:][2]['weight'])
w = [x *10 for x in b]
colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='green',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig26.png')
document_list,titles,word_count=load_data("","covid-psychology-large.txt")
Number of words in text file : 183592 Total Number of Documents: 2
clean_text=preprocess_data(document_list)
['international', 'sociology', 'author', 'article', 'reuse', 'guidelines', 'sagepub', 'journals', 'permissions', '0268580920948807', 'journals', 'sagepub', 'psychology', 'politics', 'covid', 'misinfodemics', 'people', 'believe', 'misinfodemics', 'sonia', 'mukhtar', 'university', 'management', 'technology', 'lahore', 'pakistan', 'abstract', 'misinfodemics', 'related', 'covid', 'negatively', 'impacted', 'people', 'lives', 'adverse', 'health', 'psycho', 'sociopolitical', 'outcomes', 'scientific', 'community', 'seeks', 'communicate', 'evidence', 'based', 'information', 'regarding', 'misplaced', 'preventive', 'strategies', 'misinformed', 'helpseeking', 'behaviors', 'global', 'multifaceted', 'systems', 'secondary', 'emerged', 'effects', 'misinfodemics', 'public', 'published', 'articles', 'pubmed', 'embase', 'google', 'scholar', 'elsevier', 'about', 'covid', 'related', 'misinfodemics', 'considered', 'reviewed', 'article', 'review', 'examines', 'mechanisms', 'operational', 'structure', 'prevalence', 'predictive', 'factors', 'effects', 'responses', 'potential', 'curtailing', 'strategies', 'misinfodemics', 'covid', 'present', 'article', 'shows', 'popular', 'variants', 'covid', 'misinfodemics', 'could', 'joint', 'product', 'psychological', 'predisposition', 'which', 'either', 'reject', 'information', 'experts', 'perceive', 'crisis', 'situation', 'product', 'misinfodemics', 'mechanisms', 'partisan', 'ideological', 'motivations', 'psychological', 'foundations', 'political', 'disposition', 'misinfodemics', 'implications', 'development', 'strategies', 'designed', 'curtail', 'negative', 'consequences', 'public', 'health', 'keywords', 'conspiracy', 'theories', 'covid'] ['health', 'communication', 'misinformation', 'mistrust', 'science', 'mechanisms', 'misinfodemics', 'modern', 'mainstream', 'social', 'media', 'become', 'primary', 'source', 'information', 'people', 'around', 'world', 'misinfodemics', 'surrounding', 'corresponding', 'author', 'sonia', 'mukhtar', 'university', 'management', 'technology', 'block', 'phase', 'johar', 'lahore', 'punjab', '54770', 'pakistan', 'email', 'sonia', 'mukhtar12', 'gmail', '948807iss0010', '0268580920948807international', 'sociologymukhtar', 'research', 'article2020', 'article', 'international', 'sociology', 'covid', 'pandemic', 'challenging', 'ongoing', 'coronavirus', 'covid', 'pandemic', 'outbreak', 'highlighted', 'interconnectedness', 'modern', 'globalized', 'world', 'where', 'public', 'health', 'threats', 'extend', 'beyond', 'their', 'point', 'origin', 'unvarying', 'reliance', 'misplaced', 'confidence', 'media', 'platforms', 'during', 'lockdown', 'quarantine', 'isolation', 'social', 'distancing', 'virtual', 'communication', 'become', 'major', 'source', 'interaction', 'holman', 'covid', 'pandemic', 'outbreak', 'escalated', 'challenges', 'healthcare', 'social', 'educational', 'economic', 'political', 'environmental', 'cultural', 'socioeconomic', 'systems', 'world', 'gained', 'momentum', 'innumerable', 'misinfodemics', 'mechanisms', 'rumors', 'myths', 'superstitions', 'conspiracy', 'theories', 'claims', 'hoaxes', 'false', 'misinformation', 'polarization', 'mistrust', 'science', 'times', 'crisis', 'absence', 'checking', 'misinformation', 'misleading', 'content', 'false', 'context', 'manipulated']
# LSA Model
number_of_topics=1
words=word_count
model=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_psychology =dict(model.show_topic(0, topn=words))##dict and encoding matrix values
dict(model.show_topic(0, topn=5))
{'misinfodem': 0.5033743541941372,
'covid': 0.3629711503323511,
'articl': 0.214592519351975,
'commun': 0.14837863098037743,
'health': 0.14837863098037662}
from wordcloud import WordCloud
text = dict(model.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph24.png')
# LSA Model
number_of_topics = 2 #
words = word_count
model = create_gensim_lsa_model(clean_text,number_of_topics,words)
a=pd.DataFrame()
for i in range(number_of_topics):
words_psy =dict(model.show_topic(i, topn=words))
b=pd.DataFrame(words_psy,index=[i])
a=a.append(b)
a=a.transpose()
top_words = dict(model.show_topic(0,topn=5))
df = pd.DataFrame(columns=list(top_words.keys()),
index = list(top_words.keys()))
l = list(top_words.keys())
print('cosine-similarity')
for i in l:
for j in l:
matrix=np.array([a.loc[i],a.loc[j]])
n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
df.loc[[i],[j]] = n[0,1]
print(df)
dfi.export(df, 'df_styled27.png')
cosine-similarity
misinfodem covid articl commun health
misinfodem 1 0.976353 0.993795 0.755596 0.755596
covid 0.976353 1 0.946251 0.879335 0.879335
articl 0.993795 0.946251 1 0.678051 0.678051
commun 0.755596 0.879335 0.678051 1 1
health 0.755596 0.879335 0.678051 1 1
df = df.apply(pd.to_numeric, errors='coerce')
df=df.round(4)
l = list(top_words.keys())
df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.5)}
a=list(G.edges(data=True))
b=[]
for i in a:
b.append(i[:][2]['weight'])
w = [x *10 for x in b]
colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='r',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig27.png')
def load_data(path,file_name):
"""
Input : path and file_name
Purpose: loading text file
Output : list of paragraphs/documents and
title(initial 100 words considred as title of document)
"""
documents_list = []
titles=[]
with open( os.path.join(path, file_name) ,"r",encoding='UTF8') as fin:
data = fin.read()
words = data.split()
fin.seek(0)
print('Number of words in text file :', len(words))
for line in fin.readlines():
text = line.strip()
if text !='':
documents_list.append(text)
print("Total Number of Documents:",len(documents_list))
titles.append( text[0:min(len(text),100)] )
return documents_list,titles,len(words)
document_list,titles,word_count=load_data("","covid-science-large.txt")
Number of words in text file : 274941 Total Number of Documents: 31577
clean_text=preprocess_data(document_list)
['covidgr', 'dataset', 'covid', 'sdnet'] ['methodology', 'predicting', 'covid'] ['based', 'chest', 'images'] ['tabik', 'gómez', 'martín', 'rodríguez', 'sevillano', 'garcía', 'charte'] ['guirado', 'suárez', 'luengo', 'valero', 'gonzález', 'garcía', 'villanova'] ['olmedo', 'sánchez', 'herrera'] ['abstract', 'currently', 'coronavirus', 'disease', 'covid'] ['infectious', 'diseases', 'century', 'diagnosed', 'using', 'testing', 'scans'] ['chest', 'images', 'computed', 'tomography'] ['scanners', 'testing', 'available'] ['medical', 'centers', 'hence', 'cases', 'images'] ['become', 'effective', 'assisting', 'clinicians', 'making', 'decisions', 'learning', 'neural', 'networks'] ['great', 'potential', 'building', 'covid', 'triage', 'systems'] ['detecting', 'covid', 'patients', 'especially', 'patients'] ['severity', 'unfortunately', 'current', 'databases', 'allow'] ['building', 'systems', 'highly', 'heterogeneous'] ['biased', 'towards', 'severe', 'cases', 'article', 'threefold', 'demystify', 'sensitivities', 'achieved'] ['recent', 'covid', 'classification', 'models', 'under'] ['close', 'collaboration', 'hospital', 'universitario', 'clínico'] ['cecilio', 'granada', 'spain', 'built', 'covidgr', 'homogeneous', 'balanced', 'database', 'includes', 'levels'] ['manuscript', 'received', 'september', 'revised', 'october'] ['accepted', 'november', 'publication', 'november'] ['current', 'version', 'december', 'supported'] ['project', 'deepscop', 'ayudas', 'fundación', 'equipos', 'investigación', 'científica', 'covid19_rx', 'ayudas', 'fundación'] ['equipos', 'investigación', 'científica', 'covid'] ['spanish', 'ministry', 'science', 'technology', 'under'] ['project', 'tin2017', '89517', 'tabik', 'supported', 'ramon', 'cajal'] ['programme', '18136', 'gómez', 'supported'] ['programme', 'fpu16', '04765', 'charte', 'supported'] ['programme', 'fpu17', '04069', 'suárez', 'supported', 'programme', 'fpu18', '05989', 'supported', 'european', 'research'] ['council', 'grant', 'agreement', 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['count'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['12230'] ['15535'] [] [] [] [] [] [] [] [] [] [] [] [] [] ['14291'] ['19916'] ['24897'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['12319'] ['15953'] ['18584'] [] [] [] [] [] [] [] [] [] [] ['11823'] ['19101'] ['29100'] ['44582'] ['62357'] ['77793'] [] ['denote', 'number', 'values', 'values'] ['standard', 'deviation', 'values', 'minimum', 'value'] ['maximum', 'value', 'first', 'quartile', 'second', 'quartile'] ['third', 'quartile', 'respectively', 'these', 'notations'] ['table', 'statistical', 'description'] ['considered', 'dataset', 'objective', 'analysis'] ['correlation', 'between', 'weeki', 'weeki5'] ['confirmed', 'cases', 'different', 'states', 'through'] ['analysis', 'observed', 'there', 'strong', 'correlation'] ['between', 'complete', 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[] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['logq0'] [] [] [] [] [] [] [] [] [] [] ['volume', '281li', 'hierarchical', 'pooling', 'strategy', 'optimization', 'accelerating', 'asymptomatic', 'covid', 'screening'] ['since', 'needs', 'integer'] [] ['logq0'] [] [] [] [] [] [] [] [] [] [] [] ['which', 'verified', 'consistent', 'table'] ['joint', 'optimization'] ['optimal', 'group', 'section', 'obtained', 'based'] ['assumption', 'method', 'intra', 'group', 'acceleration'] ['reduced', 'number'] ['group', 'tests'] ['group', 'using', 'intra', 'group', 'acceleration', 'likely'] ['increased', 'which', 'creates', 'improving'] ['speedup', 'increased', 'group', 'certainly', 'affects', 'choice'] ['optimal', 'subgroup', 'fortunately', 'based'] ['analytical', 'expressions', 'section', 'possible'] ['conduct', 'joint', 'optimization', 'inter', 'group', 'acceleration'] ['intra', 'group', 'acceleration', 'simultaneously'] ['optimal', 'group', 'optimal', 'subgroup'] ['inter', 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'scale'] ['multilayer', 'perceptron', 'classification', 'posed', 'covid', 'recognition', 'pipeline', 'modified'] ['replacing', 'another', 'classifier'] ['common', 'classifiers', 'hidden', 'layer', 'multilayer'] ['perceptron', 'problem', 'shown'] ['training', 'propagation'] ['optimization', 'technique', 'network', 'training', 'hyper', 'parameters', 'follows', 'learning'] ['moment', 'updates', 'number'] ['epochs', 'illustrates', 'network', 'configuration', 'detail'] ['network', 'configuration', 'achieved', 'performance'] ['among', 'others', 'deeper', 'shallower', 'where', 'configura', 'tions', 'suffered', 'fitting', 'while', 'shallow'] ['exhibit', 'inferior', 'learning', 'performance'] ['support', 'vector', 'machines', 'multiclass'] ['problem', 'first', 'objective', 'select', 'topology'] ['ensemble', 'learning', 'versus', 'versus', 'order'] ['optimal', 'topology', 'hyperparameters', 'parameters', 'first', 'performed', 'search'] ['following', 'variations', 'setting', 'kernel', 'function1816', 'transactions', 'neural', 'networks', 'learning', 'systems'] ['configuration'] ['table'] ['classification', 'performances', 'proposed'] ['competing', 'methods', 'covid', 'recognition'] ['rates', 'highlighted'] ['linear', 'radial', 'basis', 'function', 'constraint'] ['parameter', 'range', 'scale'] ['kernel', 'scale', 'kernel', 'range'] ['scale'] ['nearest', 'neighbor', 'finally', 'traditional'] ['approach', 'nearest', 'neighbor', 'dimen', 'sionality', 'reduction', 'similar', 'fashion', 'distance', 'metric'] ['value', 'optimized', 'prior', 'search'] ['following', 'distance', 'metrics', 'evaluated', 'block', 'cheby', 'correlation', 'cosine', 'euclidean', 'hamming', 'jaccard', 'lanobis', 'minkowski', 'standardized', 'euclidean', 'spearman'] ['metrics', 'value', 'varied', 'within', 'range'] ['scale'] ['experimental', 'results'] ['experimental', 'setup'] ['performed', 'experiments'] ['cov19', 'which', 'consists', 'normal'] ['three', 'pneumonia', 'classes', 'bacterial', 'viral', 'covid'] ['table'] ['number', 'images', 'class', 'before'] ['after', 'augmentation'] ['proposed', 'approach', 'evaluated', 'using', 'stratified', 'fivefold'] ['cross', 'validation', 'scheme', 'ratio', 'training'] ['unseen', 'folds', 'splits', 'respectively'] ['table', 'shows', 'number', 'images', 'class'] ['cov19', 'since', 'unbalanced'] ['applied', 'augmentation', 'training', 'order', 'class', 'train', 'therefore'] ['images', 'viral', 'covid', 'pneumonia', 'normal', 'classes'] ['augmented', 'number', 'bacterial', 'monia', 'class', 'train', 'image', 'generator'] ['keras', 'perform', 'augmentation', 'randomly', 'rotating'] ['images', 'range', 'randomly', 'shifting', 'images'] ['horizontally', 'vertically', 'within', 'interval'] ['total', 'images'] ['train', 'unseen', 'respectively'] ['experimental', 'evaluations'] ['performed', 'using', 'matlab', 'version', '2019a', 'running'] ['intel', '8650u', 'system', 'memory'] ['other', 'methods', 'implemented', 'using'] ['tensorflow', 'library', 'python', 'nvidia', 'titan'] ['training', 'optimizer'] ['proposed', 'default', 'learning', 'parameters', 'learning'] ['moment', 'updates'] ['propagation', 'epochs', 'neither', 'search'] ['other', 'parameter', 'configuration', 'optimization'] ['performed'] ['experimental', 'results'] ['network', 'configurations'] ['accordingly', 'compact', 'designs'] ['csen1', 'csen2', 'respectively', 'first', 'network'] ['consists', 'hidden', 'convolutional', 'layers', 'first'] ['layer', 'neurons', 'second', 'activation'] ['function', 'hidden', 'layers', 'filter'] ['other', 'csen2', 'pooling'] ['additional', 'hidden', 'layer', 'neurons', 'perform', 'transposed', 'convolution', 'csen1', 'csen2', 'compared', 'against'] ['competing', 'methods', 'under', 'experimental', 'setup'] ['dictionary', 'construction', 'design'] ['images', 'class', 'augmented', 'training', 'samples'] ['stacked', 'representation', 'coeffi', 'cient', 'plane', 'shown'] ['images', 'training', 'train'] ['samples', 'class'] ['dimensional', 'reduction', 'matrix', 'compression', 'ratio'] ['therefore', 'equivalentyamaç', 'convolutional', 'sparse', 'support', 'estimator', 'based', 'covid', 'recognition'] ['table'] ['number', 'network', 'parameters', 'method'] ['dictionary', 'denoiser'] [] [] [] ['obtain', 'coarse', 'estimation', 'representation', 'sparse'] ['ideal', 'coefficients', 'hereafter'] ['networks', 'trained', 'obtain', 'class', 'information'] ['input', 'illustrated'] ['other', 'learning', 'based', 'studies'] ['literature', 'chose', 'deeper', 'network', 'compared'] ['designs', 'investigate', 'network', 'depth'] ['problem', 'reconnet', 'proposed', 'noniterative'] ['learning', 'solution', 'problem'] ['state', 'compressively', 'sensed', 'image', 'recognition'] ['consists', 'fully', 'convolutional', 'layers', 'dense'] ['layer', 'front', 'convolutional', 'which'] ['learned', 'denoiser', 'mapping'] ['convolutional', 'layers', 'responsible', 'producing'] ['reconstructed', 'signal', 'therefore', 'replacing'] ['dense', 'layer', 'denoiser', 'matrix', 'network'] ['competing', 'method'] ['modified', 'reconnet', 'input'] ['which', 'produced', 'using', 'equivalent', 'dictionary'] ['pseudo', 'inverse', 'matrix'] ['designing', 'dictionary', 'system', 'training'] ['samples', 'stacked', 'dictionary', 'samples'] ['class', 'matrix', 'based'] ['recognition', 'applied', 'features', 'therefore'] ['dictionary', 'corresponding'] ['denoiser', 'matrix'] ['framework'] ['overall', 'confusion', 'matrix', 'elements', 'formed', 'positive', 'number', 'correctly', 'detected'] ['positive', 'class', 'members', 'negative', 'number', 'rectly', 'detected', 'negative', 'class', 'samples', 'false', 'positive'] ['number', 'misclassified', 'negative', 'class', 'members', 'positive'] ['number', 'misclassified', 'positive', 'class', 'samples'] ['negative', 'missed', 'positive', 'cases', 'standard'] ['performance', 'evaluation', 'metrics', 'defined', 'follows'] ['sensitivity'] [] ['where', 'sensitivity', 'recall', 'correctly', 'detected'] ['positive', 'samples', 'positive', 'class'] ['specificity'] [] ['where', 'specificity', 'ratio', 'accurately', 'detected', 'negative'] ['class', 'samples', 'negative', 'class'] ['precision'] [] ['where', 'precision', 'correctly', 'classified', 'positive', 'class'] ['samples', 'among', 'members', 'classified', 'positive', 'sample'] ['accuracy'] [] ['table'] ['computation', 'times', 'method', 'images'] ['table'] ['overall', 'cumulative', 'confusion', 'matrix'] ['proposed', 'recognition', 'scheme'] ['where', 'accuracy', 'ratio', 'correctly', 'classified', 'elements'] ['among'] [] ['precision', 'sensitivity'] [] ['precision'] ['sensitivity'] ['where', 'score', 'defined', 'weighting', 'parameter'] ['score', 'calculated', 'which', 'harmonic'] ['average', 'precision', 'sensitivity'] ['classification', 'performance', 'proposed', 'based'] ['approach', 'competing', 'methods', 'presented', 'table'] ['easily', 'observed', 'table', 'proposed'] ['approaches', 'surpass', 'competing', 'methods', 'covid', 'recognition', 'performance', 'achieving', 'sensitivity'] ['specificity', 'shown', 'table', 'compared'] ['reconnet', 'proposed', 'designs'] ['compact', 'computationally', 'efficient', 'evident'] ['table', 'where', 'computational', 'complexity', 'measured'] ['total', 'computation', 'images', 'reported'] ['finally', 'table', 'presents', 'overall', 'cumulative', 'confusion'] ['matrix', 'proposed', 'based', 'covid', 'recognition'] ['approach', 'cov19', 'critical'] ['misclassifications', 'false', 'positives', 'misclassified'] ['covid', 'images', 'confusion', 'matrix', 'shows'] ['proposed', 'approach', 'misclassified', 'seven', 'covid', 'images'] ['misclassifications', 'still', 'viral'] ['pneumonia', 'category', 'which', 'expected', 'confusion'] ['viral', 'nature', 'covid', 'however', 'other'] ['cases', 'misclassified', 'normal', 'which', 'indeed', 'severe'] ['clinical', 'misdiagnosis', 'close', 'these', 'false', 'negatives'] ['reveals', 'indeed', 'similar'] ['normal', 'images', 'where', 'typical', 'covid', 'patterns', 'hardly'] ['visible', 'expert', 'naked', 'possible', 'these'] ['images', 'patients', 'early', 'stages'] ['covid'] ['discussion'] ['versus'] ['compared', 'against', 'particular', 'based'] ['classification', 'advantages', 'computational', 'efficiency'] ['superior', 'covid', 'recognition', 'performance'] ['computational', 'efficiency', 'comes', 'larger'] ['dictionary', 'matrix', 'used1818', 'transactions', 'neural', 'networks', 'learning', 'systems'] ['proposed', 'covid', 'recognition', 'scheme'] ['table'] ['performance', 'algorithm', 'dictionary'] ['class'] ['hence', 'requires', 'computations'] ['terms', 'matrix', 'vector', 'multiplications', 'furthermore', 'saving'] ['trainable', 'parameters', 'light', 'dictionary', 'matrix'] ['coefficients', '1280k', 'device', 'memory'] ['efficient', 'compared', 'saving', 'coefficients', '4521k', 'larger'] ['dictionary'] ['further', 'analysis', 'tested', 'framework'] ['using', 'light', 'dictionary'] ['based', 'recognition', 'called', 'light'] ['table', 'performance', 'further'] ['reduced', 'there', 'significant', 'improvement', 'concerning'] ['computational', 'comes', 'creating', 'deeper'] ['convolutional', 'layers', 'instead', 'using', 'designs'] ['modified', 'reconnet', 'results', 'presented', 'table'] ['shows', 'compact', 'structures', 'indeed', 'preferable'] ['achieve', 'superior', 'classification', 'performances', 'compared'] ['deeper', 'networks'] ['compact', 'versus', 'csens'] ['representation', 'based', 'classifications', 'known', 'provid', 'satisfactory', 'performance', 'comes', 'limited'] ['other', 'artificial', 'usually', 'require'] ['large', 'training', 'achieve', 'satisfactory', 'generalization'] ['capability'] ['representation', 'based', 'dictionary', 'classification', 'scheme'] ['dictionary', 'getting', 'bigger', 'increase', 'number'] ['training', 'samples', 'computational', 'complexity', 'method'] ['drastically', 'increases', 'proposed', 'alternative'] ['approach', 'handle', 'moderate', 'scarce'] ['compact', 'possible', 'structures', 'dictionary', 'based'] ['classification'] ['since', 'there', 'other', 'learning', 'based', 'method', 'except'] ['literature', 'chose', 'reconnet', 'possible'] ['competing', 'algorithm', 'problem', 'explained', 'detail'] ['section', 'reconnet', 'fully', 'convolution', 'layers'] ['ablation', 'study', 'hidden', 'layers'] ['proposed', 'models', 'compare', 'csen3', 'csen4', 'obtained', 'adding', 'hidden', 'layers'] ['csen2', 'respectively', 'after', 'transposed', 'convolutional', 'layer'] ['table'] ['performance', 'alternative', 'deeper', 'designs', 'compared'] ['compact', 'csens'] ['table'] ['number', 'network', 'parameters', 'competing', 'networks'] ['additional', 'layers', 'neurons', 'activation', 'functions'] ['filter', 'observe', 'tables'] ['proposed', 'compact', 'designs', 'csen1', 'csen2'] ['surpass', 'deeper', 'counterparts', 'performance'] ['required', 'number', 'parameters'] ['conclusion'] ['commonly', 'methods', 'covid', 'diagnosis'] ['namely', 'certain', 'limitations', 'backs', 'processing', 'times', 'unacceptably'] ['misdiagnosis', 'rates', 'these', 'drawbacks', 'shared'] ['recent', 'works', 'literature', 'based', 'learning'] ['scarcity', 'covid', 'cases', 'although'] ['learning', 'based', 'recognition', 'techniques', 'dominant', 'puter', 'vision', 'where', 'achieved', 'state', 'performance'] ['their', 'performance', 'degrades', 'scarcity', 'which'] ['reality', 'problem', 'study', 'address'] ['limitations', 'proposing', 'robust', 'highly', 'accurate'] ['covid', 'recognition', 'approach', 'directly', 'images'] ['proposed', 'approach', 'based'] ['bridge', 'between', 'learning', 'models', 'representation', 'based', 'methods', 'dictionary'] ['training', 'samples', 'learn', 'direct', 'mapping', 'query'] ['samples', 'sparse', 'support', 'representation', 'coefficients'] ['unique', 'ability', 'having', 'advantage', 'compact'] ['network', 'proposed', 'based', 'covid', 'recognition'] ['systems', 'surpass', 'competing', 'methods', 'achieve'] ['sensitivity', 'specificity', 'furthermore', 'yield'] ['computationally', 'efficient', 'scheme', 'terms', 'speed'] ['memory'] ['acknowledgment'] ['authors', 'would', 'thank', 'following', 'medical'] ['doctor', 'their', 'generous', 'feedbacks', 'continuousyamaç', 'convolutional', 'sparse', 'support', 'estimator', 'based', 'covid', 'recognition'] ['proof', 'reading', 'khalid', 'hameed', 'medical'] ['center', 'qatar', 'tahir', 'hamid', 'consultant', 'cardiologist'] ['hamad', 'medical', 'corporation', 'hospital', 'weill', 'cornell'] ['medicine', 'qatar', 'rashid', 'mazhar', 'hamad'] ['medical', 'corporation', 'hospital', 'qatar']
# LSA Model
number_of_topics=1
words=word_count
model=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_law =dict(model.show_topic(0, topn=words))##dict and encoding matrix values
dict(model.show_topic(0, topn=5))
{'covid': 0.8597144274029378,
'imag': 0.24460862250200113,
'model': 0.21195199497555547,
'detect': 0.14844764626959342,
'volum': 0.09483931504535177}
from wordcloud import WordCloud
text = dict(model.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph25.png')
# LSA Model
number_of_topics = 100 #
words = word_count
model = create_gensim_lsa_model(clean_text,number_of_topics,words)
a=pd.DataFrame()
for i in range(number_of_topics):
words_science =dict(model.show_topic(i, topn=words))
b=pd.DataFrame(words_science,index=[i])
a=a.append(b)
a=a.transpose()
top_words = dict(model.show_topic(0,topn=5))
df = pd.DataFrame(columns=list(top_words.keys()),
index = list(top_words.keys()))
l = list(top_words.keys())
print('cosine-similarity')
for i in l:
for j in l:
matrix=np.array([a.loc[i],a.loc[j]])
n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
df.loc[[i],[j]] = n[0,1]
print(df)
dfi.export(df, 'df_styled28.png')
cosine-similarity
covid imag model detect volum
covid 1 0.00104162 9.92486e-05 0.00370578 0.00347403
imag 0.00104162 1 0.000729477 -0.0011778 0.00282604
model 9.92486e-05 0.000729477 1 -0.00173646 -0.00473556
detect 0.00370578 -0.0011778 -0.00173646 1 0.0169738
volum 0.00347403 0.00282604 -0.00473556 0.0169738 1
df = df.apply(pd.to_numeric, errors='coerce')
df=df.round(4)
l = list(top_words.keys())
df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.5)}
a=list(G.edges(data=True))
b=[]
for i in a:
b.append(i[:][2]['weight'])
w = [x *100 for x in b]
colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='green',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig28.png')
document_list,titles,word_count=load_data("","covid-psychology-large.txt")
Number of words in text file : 183592 Total Number of Documents: 18522
clean_text=preprocess_data(document_list)
['international', 'sociology'] [] ['author'] ['article', 'reuse', 'guidelines'] ['sagepub', 'journals', 'permissions'] ['0268580920948807'] ['journals', 'sagepub'] ['psychology', 'politics'] ['covid', 'misinfodemics'] ['people'] ['believe', 'misinfodemics'] ['sonia', 'mukhtar'] ['university', 'management', 'technology', 'lahore', 'pakistan'] ['abstract'] ['misinfodemics', 'related', 'covid', 'negatively', 'impacted', 'people', 'lives', 'adverse'] ['health', 'psycho', 'sociopolitical', 'outcomes', 'scientific', 'community', 'seeks', 'communicate'] ['evidence', 'based', 'information', 'regarding', 'misplaced', 'preventive', 'strategies', 'misinformed', 'helpseeking', 'behaviors', 'global', 'multifaceted', 'systems', 'secondary', 'emerged', 'effects'] ['misinfodemics', 'public', 'published', 'articles', 'pubmed', 'embase', 'google', 'scholar', 'elsevier'] ['about', 'covid', 'related', 'misinfodemics', 'considered', 'reviewed', 'article'] ['review', 'examines', 'mechanisms', 'operational', 'structure', 'prevalence', 'predictive', 'factors', 'effects'] ['responses', 'potential', 'curtailing', 'strategies', 'misinfodemics', 'covid', 'present', 'article'] ['shows', 'popular', 'variants', 'covid', 'misinfodemics', 'could', 'joint', 'product'] ['psychological', 'predisposition', 'which', 'either', 'reject', 'information', 'experts', 'perceive'] ['crisis', 'situation', 'product', 'misinfodemics', 'mechanisms', 'partisan', 'ideological', 'motivations'] ['psychological', 'foundations', 'political', 'disposition', 'misinfodemics', 'implications'] ['development', 'strategies', 'designed', 'curtail', 'negative', 'consequences', 'public', 'health'] ['keywords'] ['conspiracy', 'theories', 'covid', 'health', 'communication', 'misinformation', 'mistrust', 'science'] ['mechanisms', 'misinfodemics'] ['modern', 'mainstream', 'social', 'media', 'become', 'primary', 'source', 'information', 'people', 'around', 'world', 'misinfodemics', 'surrounding'] ['corresponding', 'author'] ['sonia', 'mukhtar', 'university', 'management', 'technology', 'block', 'phase', 'johar', 'lahore'] ['punjab', '54770', 'pakistan'] ['email', 'sonia', 'mukhtar12', 'gmail'] ['948807iss0010', '0268580920948807international', 'sociologymukhtar'] ['research', 'article2020'] ['article'] ['international', 'sociology'] ['covid', 'pandemic', 'challenging', 'ongoing', 'coronavirus'] ['covid', 'pandemic', 'outbreak', 'highlighted', 'interconnectedness', 'modern'] ['globalized', 'world', 'where', 'public', 'health', 'threats', 'extend', 'beyond', 'their', 'point', 'origin'] ['unvarying', 'reliance', 'misplaced', 'confidence', 'media', 'platforms', 'during'] ['lockdown', 'quarantine', 'isolation', 'social', 'distancing', 'virtual', 'communication'] ['become', 'major', 'source', 'interaction', 'holman'] ['covid', 'pandemic', 'outbreak', 'escalated', 'challenges'] ['healthcare', 'social', 'educational', 'economic', 'political', 'environmental', 'cultural', 'socioeconomic', 'systems', 'world', 'gained', 'momentum', 'innumerable'] ['misinfodemics', 'mechanisms', 'rumors', 'myths', 'superstitions', 'conspiracy', 'theories'] ['claims', 'hoaxes', 'false', 'misinformation', 'polarization', 'mistrust', 'science'] ['times', 'crisis', 'absence', 'checking', 'misinformation', 'misleading', 'content', 'false'] ['context', 'manipulated', 'fabricated', 'material', 'imposter', 'documents', 'disinformation', 'regarding', 'etiology', 'outcomes', 'misplaced', 'prevention', 'disease'] ['mukhtar', '2020a'] ['covid', 'pandemic', 'outbreak', 'saturated', 'mainstream', 'media', 'which', 'disseminate', 'information', 'local', 'global', 'scale', 'similarly', 'social', 'media', 'platforms'] ['become', 'accessible', 'source', 'information', 'numerous', 'incidents', 'initiated'] ['these', 'rumors', 'caused', 'several', 'mishaps', 'across', 'world', 'instance'] ['after', 'positive', 'diagnosis', 'covid', 'india', 'people', 'overdosed'] ['cholorquine', 'after', 'about', 'effectiveness', 'against', 'covid', 'proliferated', 'nigeria', 'busari', 'adebayo', 'there', 'failure'] ['distinction', 'between', 'empirically', 'based', 'scientific', 'truths', 'fabricated', 'unconfirmed'] ['science', 'conspiracy', 'theories', 'among', 'general', 'public', 'reports', 'reduce', 'legitimacy', 'scientific', 'discoveries', 'regarding', 'vaccine', 'covid'] ['create', 'social', 'stigma', 'resulting', 'xenophobia', 'chinese', 'sentiment', 'racism', 'marginalization', 'reduced', 'compliance', 'adherence', 'quarantine', 'adverse', 'health'] ['psychosocial', 'impacts', 'aguilera', '2020a', '2020b', 'these', 'considerations', 'become', 'exacerbated', 'during', 'lockdown', 'leading', 'people', 'fringe'] ['popular', 'opinion', 'spend', 'social', 'media', 'people', 'trying'] ['sense', 'their', 'changed', 'lives', 'proclaimed', 'celebrities', 'identified', 'social'] ['media', 'stars', 'politicians', 'mainstream', 'media', 'public', 'figures', 'propagating', 'their'] ['subjective', 'interpretations', 'events', 'situation', 'covid', 'pandemic', 'misinfodemics', 'lederer'] ['scientific', 'studies', 'previous', 'disease', 'outbreaks', 'demonstrated', 'misinformation', 'represents', 'secondary', 'challenge', 'public', 'health', 'efforts', 'controlling', 'epidemic', 'pandemic', 'earnshaw', 'kalichman', 'individuals', 'endorse'] ['misinformation', 'about', 'disease', 'likely', 'follow', 'public', 'health', 'instructions', 'during'] ['ebola', 'outbreak', 'respondents', 'credence', 'conspiracy', 'theories'] ['alleged', 'would', 'likely', 'support', 'during', 'disease', 'outbreak'] ['national', 'cancer', 'institute', 'study', 'montanaro', 'conducted'] ['respondents', 'reported', 'trust', 'medical', 'healthcare', 'professionals', 'contrast', 'study', 'where', 'majority', 'respondents', 'reported', 'mistrust', 'information'] ['about', 'covid', 'current', 'administration', 'mainstream', 'media', 'outlets'] ['times'] ['mukhtar'] ['psychology', 'misinfodemics'] ['adaptive', 'emotion', 'which', 'serves', 'mobilize', 'energy', 'towards', 'potential', 'actual'] ['perceived', 'threat', 'mertens', 'however', 'excessive'] ['detrimental', 'effects', 'individual', 'mental', 'health', 'problems', 'anxiety'] ['phobia', 'community', 'level', 'hoarding', 'panic', 'shopping', 'xenophobia'] ['insufficient', 'cause', 'individual', 'incompliance', 'nonadherence', 'towards', 'quarantine', 'isolation', 'community', 'reckless', 'implementation'] ['policies', 'ignores', 'risks', 'socioeconomic', 'infrastructure', 'likewise', 'interpersonal'] ['safety', 'measures', 'mitigate', 'certain', 'threats', 'transmission', 'disease', 'paradoxically', 'enhance', 'transmission', 'excessive', 'health', 'anxiety', 'psychosocial'] ['issues', 'similarly', 'social', 'safety', 'measures', 'lockdowns', 'curfews', 'control', 'transmission', 'prolonged', 'stringent', 'measures', 'could', 'negative', 'consequences', 'socioeconomic', 'impact', 'mental', 'health', 'impact'] ['efforts', 'misinfodemics', 'process', 'increased', 'because', 'adverse'] ['effects', 'public', 'health', 'communication', 'encourage', 'adoption', 'sustainable'] ['preventive', 'measures', 'manage', 'social', 'physical', 'distancing', 'psychological', 'health'] ['resilience', 'socioeconomic', 'conditions', 'address', 'stigma', 'prejudice', 'discrimination'] ['inequalities', 'mukhtar', '2020b', 'among', 'various', 'factors', 'psychological', 'vulnerability', 'mukhtar', '2020c', 'mukhtar', 'mahmood', 'propensity', 'follow', 'popular'] ['opinion', 'excessive', 'social', 'media', 'presence', 'predisposition', 'anxiety'] ['uncertainty', 'intolerance', 'unpredictability', 'coronavirus', 'brings', 'levels', 'uncertainty', 'inability', 'uncertainty', 'exponentially', 'higher', 'causing', 'anxiety'] ['health', 'related', 'worry', 'situation', 'exacerbated', 'exposure', 'plethora'] ['information', 'including', 'misinformation', 'disinformation', 'about', 'impending'] ['threat', 'mainstream', 'media', 'social', 'media', 'alike', 'rosser', 'perceived', 'threat'] ['information', 'elevates', 'repeated', 'engagement', 'trauma', 'related', 'media', 'content'] ['several', 'hours', 'daily', 'culminates', 'acute', 'stress', 'emotional', 'distress', 'consequently'] ['either', 'increase', 'virus', 'cause', 'insensitivity', 'towards', 'course'] ['identification', 'monitoring', 'internet', 'centrifugal', 'clarification', 'filter'] ['accuracy', 'content', 'become', 'challenging', 'increasing', 'number', 'people'] ['social', 'media', 'platforms', 'challenging', 'problems', 'misinformation', 'disinformation', 'conspiracy', 'theories', 'social', 'media', 'emulsify', 'false'] ['misleading', 'click', 'content', 'world', 'health', 'organization', 'launched'] ['mythbuster', 'feature', 'website', 'countermeasure', 'spread', 'unauthentic'] ['social', 'media', 'people', 'reported', 'highest', 'media', 'exposure', 'reported', 'higher'] ['acute', 'stress', 'holman', 'media', 'exposure', 'accumulates', 'emerging', 'threats'] ['repeated', 'exposure', 'these', 'events', 'increase', 'symptoms', 'distress', 'worry', 'fixation'] ['mentality', 'misplaced', 'conviction', 'people', 'vulnerable', 'media', 'exposure', 'distress'] ['alongside', 'amount', 'media', 'exposure', 'content', 'exposure', 'matters'] ['exposure', 'tragic', 'events', 'graphic', 'images', 'conspiracy', 'theories', 'violence', 'could'] ['instigate', 'posttraumatic', 'stress', 'future', 'which', 'personal'] ['functioning', 'recent', 'years', 'mainstream', 'media', 'electronic', 'print', 'social', 'media'] ['established', 'themselves', 'beyond', 'government', 'channel', 'global'] ['international', 'sociology'] ['population', 'developed', 'affinity', 'channels', 'particular', 'while', 'older'] ['population', 'mainly', 'relies', 'traditional', 'channels', 'youth', 'social', 'media'] ['tiktok', 'instagram', 'youtube', 'twitter', 'facebook', 'whatsapp', 'social', 'media'] ['become', 'conduit', 'spreading', 'rumors', 'deliberate', 'misinformation', 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'linear', 'version', 'autoregressive'] ['distributed', 'model', 'applied'] ['methodology'] [] ['study', 'utilized', 'daily', 'number', 'confirmed'] ['patients', 'deaths', 'covid', 'stock', 'markets'] ['spain', 'italy', 'germany', 'period'] ['february', 'april', 'study', 'taken'] ['database', 'yahoo', 'finance', 'european', 'center'] ['disease', 'control', 'prevention', 'further', 'investigation'] ['analyzed', 'daily', 'returns'] ['london', 'stock', 'exchange', 'nomination', 'spain', 'italy'] ['germany', 'however', 'nasdaq', 'composite'] ['index', 'represents'] ['methodology'] ['investigation', 'utilized', 'stock', 'market', 'daily', 'returns'] ['dependent', 'variable', 'confirmed', 'cases', 'deaths'] ['independent', 'variable', 'daily', 'returns', 'study'] ['computed', 'follows', 'syllignakis', 'kouretas', 'shehzad'] ['sohail'] [] [] [] ['denote', 'return', 'value', 'closing'] ['price', 'previous', 'closing', 'price'] ['stock', 'market', 'respectively', 'linear', 'association', 'between', 'these'] ['variables', 'defined'] ['1sprt', 'β1usct', 'β2usdt'] ['1nsrt', 'β1usct', 'β2usdt'] ['frontiers', 'psychology', 'frontiersin', 'september', 'volume', 'article', '1924fpsyg', '01924', 'september'] ['shehzad', 'financial', 'markets', 'behavior', 'covid'] ['figure', 'deterioration', 'stock', 'markets', 'owing', 'novel', 'coronavirus'] ['frontiers', 'psychology', 'frontiersin', 'september', 'volume', 'article', '1924fpsyg', '01924', 'september'] ['shehzad', 'financial', 'markets', 'behavior', 'covid'] ['figure', 'variations', 'global', 'financial', 'crisis'] ['covid', 'crisis'] ['1ibert', 'β1spct', 'β2spdt'] ['1ftmrt', 'β1itlct', 'β2itldt'] ['1daxrt', 'β1gerct', 'β2gerdt'] ['1lsert', 'β1ukct', 'β2ukdt'] ['where', 'signifies', 'first', 'difference', 'β0β1β2'] ['independent', 'parameters', 'however'] ['defines', 'return', 'values', 'nasdaq'] ['composite', 'index'] ['london', 'stock', 'exchange', 'respectively', 'moreover'] ['indicate', 'confirmed', 'cases', 'covid', 'spain', 'italy', 'germany', 'respectively'] ['further', 'refer', 'confirmed'] ['deaths', 'covid', 'spain', 'italy', 'germany'] ['respectively'] ['ascertain', 'short', 'asymmetries'] ['study', 'employed', 'linear', 'autoregressive', 'distributed'] ['nardl', 'model', 'introduced', 'model'] ['performs', 'small', 'number', 'observations'] ['applied', 'mixed', 'level', 'stationary'] ['evaluates', 'linearity', 'cointegration', 'between', 'variables'] ['equation', 'shared', 'short', 'variations'] ['asymmetries', 'after', 'taking', 'parameters', 'account'] ['nardl', 'model', 'extended'] ['pesaran', 'model', 'hence'] ['nardl', 'model', 'stock', 'market', 'specified'] ['1sprt'] [] [] [] ['1sprt'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['γ2ausc'] ['γ2busc'] ['γ3ausd'] [] [] [] ['1nsrt'] [] [] [] ['1nsrt'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['γ1nsrt', 'γ2ausc'] ['γ2busc'] [] [] ['γ3busd'] [] ['1ibert'] [] [] [] ['1ibert'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['γ1ibert', 'γ2aspc'] ['γ2bspc'] [] [] ['γ3bspd'] [] ['1ftmrt'] [] [] [] ['1ftmrt'] [] [] [] ['1itlc'] [] [] [] [] [] ['1itlc'] [] [] [] [] [] ['1itld'] [] [] [] [] [] [] ['γ1ftmrt', 'γ2aitlc'] [] [] [] [] [] [] [] ['1daxrt'] [] [] [] ['1daxrt'] [] [] [] ['1gerc'] [] [] [] [] [] ['1gerc'] [] [] [] [] ['1gerd'] [] [] [] [] [] [] ['γ1daxrt', 'γ2agerc'] ['γ2bgerc'] [] [] ['γ3bgerd'] [] ['frontiers', 'psychology', 'frontiersin', 'september', 'volume', 'article', '1924fpsyg', '01924', 'september'] ['shehzad', 'financial', 'markets', 'behavior', 'covid'] ['1lsert'] [] [] [] ['1lsert'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['γ1lsert', 'γ2aukc'] ['γ2bukc'] [] [] ['γ3bukd'] [] ['ϑ2bϑ3aand', 'short', 'factors', 'while'] [] [] ['indicates', 'parameters'] ['whereas', 'symbolizes', 'number', 'model'] ['based', 'akaik', 'information', 'criterion', 'schwarz', 'information'] ['criterion', 'above', 'stated', 'equations', 'assumed'] ['confirmed', 'number', 'cases', 'deaths', 'covid'] ['asymmetrical', 'impact', 'stock', 'returns'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['designates', 'positive', 'shock', 'though'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['implies', 'negative', 'shock', 'variable'] ['computed', 'follows'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['delineates', 'positive', 'shock', 'variable', 'denotes'] ['asymmetric', 'distributive', 'error', 'correction'] ['these', 'factors', 'written', 'follows'] ['1sprt'] [] [] [] ['1sprt'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['φ1ectt'] ['1nsrt'] [] [] [] ['1nsrt'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['φ1ectt'] ['1ibert'] [] [] [] ['1ibert'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['φ1ectt'] ['1ftmrt'] [] [] [] ['1ftmrt'] [] [] [] ['1itlc'] [] [] [] [] [] ['1itlc'] [] [] [] [] [] ['1itld'] [] [] [] [] [] [] ['φ1ectt'] ['1daxrt'] [] [] [] ['1daxrt'] [] [] [] ['1gerc'] [] [] [] [] [] ['1gerc'] [] [] [] [] [] ['1gerd'] [] [] [] [] [] [] ['φ1ectt'] ['1lsert'] [] [] [] ['1lsert'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['φ1ectt'] ['directs', 'error', 'correction'] ['cointegration', 'among', 'variables', 'examined', 'through', 'bound'] ['approach', 'pesaran', 'method', 'relies', 'evaluate', 'hypothesis'] ['purpose', 'pesaran', 'defined', 'bounds'] ['upper', 'lower', 'bound', 'estimated', 'statistics'] ['higher', 'upper', 'bound', 'limit', 'hypothesis'] ['cointegration', 'rejected', 'however', 'projected', 'value', 'lower', 'lower', 'bound', 'limit', 'hypothesis', 'cannot'] ['rejected', 'besides', 'value', 'remains', 'between'] ['limits', 'results', 'conclusive', 'project'] ['employed', 'verify', 'asymmetric'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['andshort'] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] [] ['asymmetric'] ['relationship', 'between', 'study', 'variables'] ['frontiers', 'psychology', 'frontiersin', 'september', 'volume', 'article', '1924fpsyg', '01924', 'september'] ['shehzad', 'financial', 'markets', 'behavior', 'covid'] ['study', 'findings'] ['descriptive', 'summary', 'showed', 'table', 'during', 'covid', 'stock', 'returns', 'negative', 'further', 'skewness'] ['values', 'these', 'markets', 'negative', 'except'] ['kurtosis', 'which', 'predicts', 'chances', 'these'] ['table', 'presents', 'results', 'augmented', 'dickey', 'fuller'] ['dickey', 'fuller', 'phillip', 'perron'] ['phillips', 'perron', 'showed', 'study', 'variables'] ['diverse', 'stationary', 'levels', 'variable'] ['cointegrated'] ['bound', 'cointegration', 'fallouts'] ['outcomes', 'bound', 'displayed', 'table', 'disclosed'] ['significant', 'statistical', 'evidence', 'association', 'between'] ['study', 'variables', 'equation', 'statistics', 'joint'] ['significance', 'lagged', 'level', 'parameters', 'stated'] ['respectively', 'these', 'values'] ['surpass', 'upper', 'bound', 'limits', 'implying'] ['cointegration', 'exists', 'among', 'economic', 'crisis', 'health'] ['crisis', 'financial', 'markets', 'equation'] ['short', 'asymmetric'] ['cointegration', 'reckoning'] ['table', 'exhibited', 'upshots', 'statistics'] ['particularized', 'linear', 'association'] ['however', 'revealed', 'short', 'asymmetric'] ['affiliation', 'besides', 'caused', 'asymmetric'] ['influence', 'short', 'periods'] ['while', 'possessed', 'short', 'asymmetry', 'additionally'] ['table', 'descriptive', 'statistics'] [] ['41737', '08997', '70833', '28187'] ['skewness', '68003', '687765', '641313', '027343', '392073', '081854', '35422', '186996'] ['kurtosis', '36907', '166164', '206693', '903565', '778953', '75376', '380136', '389965'] ['jarque', '26244', '69524', '87966', '47298', '76708'] ['probability', '00434', '000161'] [] ['18523', '54717', '42086', '40245'] ['skewness', '927378', '53985', '120857', '230936', '65869', '232889', '058817', '31595'] ['kurtosis', '43888', '59924', '573906', '120135', '135106', '868864', '472667', '04434'] ['jarque', '44518', '49843', '45175', '59309', '03773', '08892'] ['probability', '000013', '003185', '000121', '000872'] ['source', 'author', 'calculation'] ['table'] ['statistics', 'statistics'] ['variable', 'level', 'level'] ['893713', '54426', '057616'] ['293963', '969976', '971366', '51473'] ['935247', '359161', '147762', '393867'] ['631659', '55646', '451458'] ['127702', '174362', '400067', '35456'] ['300231', '849862', '488626', '372171'] ['27056', '985616', '446574', '65801'] ['506727', '32241', '159422', '09496'] ['552953', '028486', '374729', '97616'] ['775255', '591517', '768878', '87539'] ['816325', '578091', '746256', '61778'] ['429147', '36282', '571441', '31254'] ['89506', '022076', '13962'] ['847753', '567572', '310638', '46537'] ['138569', '097646', '384682', '006879'] ['66594', '40744', '64235', '28961'] ['denote', 'level', 'significance', 'respectively', 'source', 'author', 'calculation'] ['frontiers', 'psychology', 'frontiersin', 'september', 'volume', 'article', '1924fpsyg', '01924', 'september'] ['shehzad', 'financial', 'markets', 'behavior', 'covid'] ['table', 'results', 'bound'] ['statistic', 'value', 'signif', 'statistics', 'value', 'signif'] [] ['statistic', '02410', 'statistic', '165881'] [] [] [] ['statistic', '11238', 'statistic', '23325'] [] [] [] ['statistic', '490181', 'statistic', '03259'] [] [] ['source', 'author', 'calculation'] ['table'] ['asymmetry', 'short', 'asymmetry', 'asymmetry', 'short', 'asymmetry'] [] ['242605'] [] [] ['574046'] ['589403', '609162', '338933'] [] ['569179', '725822', '640751'] ['61007', '786146', '718125'] ['denote', 'level', 'significance', 'respectively', 'source', 'author', 'calculation'] ['results', 'designated', 'asymmetric'] ['relationship', 'statistics', 'enlightened'] ['short', 'linear', 'impacts'] ['short', 'linear', 'affiliation'] ['moreover', 'denotes', 'asymmetric', 'liaison'] ['hence', 'existence', 'linearity', 'study', 'variables'] ['choose', 'nardl', 'model', 'computing'] ['short', 'factors'] ['modeling', 'nardl', 'parameters'] ['linear', 'fallouts', 'presented', 'table', 'illustrated'] ['expansion', 'contraction', 'brings', 'reduction'] ['increase', 'respectively', 'indicates', 'strict', 'lockdown'] ['substantially', 'decreases', 'market', 'returns'] ['versa', 'further', 'positive', 'shock', 'caused', 'significant'] ['short', 'lagged', 'values', 'specified'] ['negative', 'shock', 'amplified', 'besides', 'lagged'] ['terms', 'described', 'uptick', 'effect', 'improved'] ['negative', 'shock', 'second', 'augmented'] ['first', 'third', 'represents', 'reverse', 'impact'] ['furthermore', 'negative', 'significant', 'value', 'stated'] ['disequilibrium', 'occurred', 'today', 'covid'] ['adjust', 'speed', 'units', 'subsequent'] ['squared', 'value', 'indicated', 'volatility'] ['owing', 'covid'] ['model', 'results', 'exhibited', 'table', 'particularized'] ['positive', 'negative', 'shocks', 'escalate'] ['while', 'enlargement', 'reduction', 'instigate', 'decrease'] ['increase', 'specifies', 'upsurge', 'health', 'crisis'] ['expressively', 'distressed', 'nasdaq', 'composite', 'index'] ['versa', 'nonetheless', 'short', 'negative', 'shock'] ['demonstrated', 'imperative', 'decrease'] ['whereas', 'expansion', 'lessens', 'negative'] ['significant', 'parameter', 'stated', 'financial', 'instability'] ['generated', 'covid', 'would', 'settled', 'speed'] ['units', 'square', 'value', 'signified'] ['instability', 'occurring', 'result', 'covid'] ['finding', 'parameters', 'model', 'table', 'identified'] ['assertive', 'shock', 'encouraging', 'effect'] ['besides', 'adverse', 'shocks', 'remain', 'insignificant', 'other'] ['escalation', 'decline', 'negative', 'positive', 'influence'] ['implying', 'strict', 'lockdown', 'considerably', 'caused'] ['financial', 'crisis', 'spain', 'period', 'short'] ['period', 'intensification', 'decline'] ['increased', 'negative', 'significant', 'value'] ['elucidated', 'today', 'disequilibrium', 'adjust'] ['speed', 'units', 'coefficient', 'square'] ['stated', 'variations', 'owing', 'covid'] ['table', 'displayed', 'fallouts', 'stock', 'market'] ['condition', 'covid', 'italy', 'stock', 'markets', 'exposed'] ['frontiers', 'psychology', 'frontiersin', 'september', 'volume', 'article', '1924fpsyg', '01924', 'september'] ['shehzad', 'financial', 'markets', 'behavior', 'covid'] ['table', 'asymmetric', 'parameters'] ['variables', 'short', 'coefficients', 'error'] ['usc_neg', '047867', '003539', '52394'] ['usc_neg', '061323', '003402', '02596'] ['usc_neg', '108895', '005569', '55339'] ['usc_neg', '072915', '003619', '14618'] ['usd_pos', '713848', '036514', '55011'] ['usd_pos', '23431', '014268', '42276'] ['usd_pos', '194744', '01369', '22562'] ['usd_pos', '334891', '029691', '27907'] ['usd_neg', '377433', '055371', '816426'] ['usd_neg', '65514', '097958', '89641'] ['usd_neg', '074692', '02737', '728986'] ['usd_neg', '650475', '235321', '51275'] ['369525', '066813', '49782'] ['squared', '912909'] ['adjusted', 'squared', '892007'] ['durbin', 'watson', '006439'] ['variables', 'coefficients', 'error'] ['usc_pos', '004491', '001667', '694157'] ['usc_neg', '054381', '014406', '774912'] ['usd_pos', '064436', '029631', '174626'] ['usd_neg', '290139', '338048', '858278'] ['181416', '264851', '684971'] ['denote', 'level', 'significance', 'respectively', 'source', 'author', 'calculation'] ['table', 'asymmetric', 'parameters'] ['variables', 'short', 'coefficients', 'error'] ['usd_neg', '039324', '019503', '016267'] ['usc_pos', '002147', '000382', '622014'] ['usc_neg', '002319', '00069', '360414'] ['usc_neg', '008198', '001064', '703282'] ['501702', '093855', '00026'] ['squared', '812596'] ['adjusted', 'squared', '800102'] ['durbin', 'watson', '130245'] ['variables', 'coefficients', 'error'] ['usd_pos', '013042', '004465', '920922'] ['usd_neg', '026413', '030567', '864107'] ['usc_pos', '001013', '00034', '977381'] ['usc_neg', '004258', '001923', '214416'] ['525513', '274541', '914153'] ['denote', 'level', 'significance', 'respectively', 'source', 'author', 'calculation'] ['reduction', 'diminished', 'amplified'] ['inferring', 'heath', 'crisis', 'imperatively', 'contributes', 'italy'] ['financial', 'instability', 'period', 'addition', 'growth'] ['enlarged', 'imperatively', 'condition'] ['italy', 'denoted', 'first', 'second', 'third', 'values'] ['harmed', 'returns', 'growth', 'negative'] ['impression', 'nonetheless', 'negative', 'shock'] ['second', 'value', 'indicated', 'encouraging', 'effect'] ['consequences', 'symbolized', 'proliferation'] ['possess', 'negative', 'ftmer', 'first'] ['second', 'value', 'definite', 'furthermore', 'decline'] ['direct', 'influence', 'negative'] ['significant', 'figure', 'itemized', 'readjust'] ['equilibrium', 'speed', 'units', 'squared', 'parameter', 'denoted', 'uncertainty', 'generated'] ['caused', 'covid'] ['frontiers', 'psychology', 'frontiersin', 'september', 'volume', 'article', '1924fpsyg', '01924', 'september'] ['shehzad', 'financial', 'markets', 'behavior', 'covid'] ['table', 'asymmetric', 'parameters'] ['variables', 'short', 'coefficients', 'error'] ['368944', '09522'] ['spd_pos', '018471', '009268'] ['spd_neg', '026216', '010675'] ['spc_pos', '001511', '000911'] ['spc_neg', '001832', '000966'] ['spc_neg', '00301', '000996'] ['spc_neg', '006948', '00129'] ['824331', '137511'] ['squared', '726141'] ['adjusted', 'squared', '694191'] ['durbin', 'watson', '154368'] ['variables', 'coefficients', 'error'] ['spd_pos', '053991', '01765'] ['spd_neg', '023488', '018017'] ['spc_pos', '006341', '001968'] ['spc_neg', '005173', '001945'] ['636028', '428132'] ['denote', 'level', 'significance', 'respectively', 'source', 'author', 'calculation'] ['table', 'asymmetric', 'parameters'] ['variables', 'short', 'coefficients', 'error'] ['27947', '138377', '019624'] ['154875', '136743', '132605'] ['295127', '113398', '602569'] ['itlc_pos', '008711', '001397', '235569'] ['itlc_pos', '015154', '003264', '642728'] ['itlc_pos', '002589', '573306'] ['itlc_neg', '001115', '001937', '575378'] ['itlc_neg', '006885', '00198', '477965'] ['itlc_neg', '003758', '001782', '109409'] ['itld_pos', '06983', '014714', '745672'] ['itld_pos', '023801', '011962', '989722'] ['itld_pos', '029575', '010511', '813763'] ['itld_neg', '002565', '012862', '199404'] ['itld_neg', '038093', '015194', '507186'] ['itld_neg', '04502', '013664', '294772'] ['959373', '163657', '862111'] ['squared', '851016'] ['adjusted', 'squared', '807197'] ['durbin', 'watson', '763297'] ['variables', 'coefficients', 'error'] ['itlc_pos', '013317', '005649', '357295'] ['itlc_neg', '001321', '002682', '492464'] ['itld_pos', '109433', '053273', '054209'] ['itld_neg', '048345', '020559', '351573'] ['500777', '455916', '098396'] ['denote', 'level', 'significance', 'respectively', 'source', 'author', 'calculation'] ['parameters', 'table', 'directed'] ['upsurge', 'diminution', 'compacted'] ['enlarge', 'moreover', 'decline'] ['improves', 'these', 'findings', 'revealed', 'lockdown'] ['health', 'crises', 'harmful', 'stock', 'markets'] ['germany', 'short', 'findings', 'described', 'second'] ['frontiers', 'psychology', 'frontiersin', 'september', 'volume', 'article', '1924fpsyg', '01924', 'september'] ['shehzad', 'financial', 'markets', 'behavior', 'covid'] ['table', 'asymmetric', 'parameters'] ['variables', 'short', 'coefficients', 'error'] ['gerc_pos', '015476', '001209', '80122'] ['gerc_pos', '012439', '001923', '467043'] ['gerc_pos', '088025', '005402', '29596'] ['gerc_pos', '057992', '003854', '04828'] ['gerc_pos', '00647', '99249'] ['gerc_neg', '05202', '004721', '11903'] ['gerc_neg', '067796', '004325', '67443'] ['gerc_neg', '044564', '003649', '21376'] ['gerc_neg', '01327', '004158', '191441'] ['gerc_neg', '183056', '01145', '98705'] ['gerd_pos', '101477', '313432', '27619'] ['gerd_pos', '265597', '35462', '84858'] ['gerd_pos', '054459', '100825', '540134'] ['gerd_pos', '261756', '386781', '18939'] ['gerd_neg', '913296', '288936', '54383'] ['gerd_neg', '64757', '850055', '56576'] ['gerd_neg', '76795', '466544', '20677'] ['gerd_neg', '21192', '08761'] ['497359', '029762', '71134'] ['squared', '945788'] ['adjusted', 'squared', '910937'] ['durbin', 'watson', '667758'] ['variables', 'coefficients', 'error'] ['gerc_pos', '016748', '035958', '465776'] ['gerc_neg', '255912', '083972'] ['gerd_pos', '28734', '569148', '548151'] ['gerd_neg', '49979', '581496'] ['440744', '93232', '617925'] ['denote', 'level', 'significance', 'respectively', 'source', 'author', 'calculation'] ['table', 'asymmetric', 'parameters'] ['variables', 'short', 'coefficients', 'error'] ['ukc_pos', '00127', '001661', '764723'] ['ukc_pos', '022345', '003248', '878603'] ['ukc_neg', '009267', '002137', '337228'] ['ukd_pos', '03503', '00746', '695883'] ['ukd_pos', '090212', '014812', '090388'] ['100754', '115104', '563147'] ['squared', '651816'] ['adjusted', 'squared', 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'recall', 'similar', 'tohow', 'epidemic', 'psychology', 'works', 'twitter'] [] ['keywords', 'supporting', 'literature', 'categories'] [] [] [] ['emotional', 'maelstrom', 'swear'] ['anger'] ['negemo'] ['these', 'categories', 'analyze'] ['complex', 'emotional', 'responses', 'traumatic', 'events'] ['characterize', 'language', 'people'] ['suffering', 'mental', 'health', 'coppersmith'] ['sadness'] ['related', 'words', 'included', 'emolex'] ['often', 'measure', 'tangible'] ['intangible', 'threats'] ['emolex'] ['death'] ['anxiety', 'panic', 'anxiety', 'category', 'study', 'ferent', 'forms', 'anxiety', 'social', 'media', 'rudzicz'] [] ['anxiety'] ['disorientation', 'definition', 'tentative', 'category', 'expresses'] ['uncertainty', 'tausczik', 'pennebaker'] ['tentative'] ['suspicion', 'suspicion', 'often', 'formalized', 'trust', 'deutsch'] [] ['trust'] ['irrationality', 'negate', 'category', 'measure'] ['cognitive', 'distorsions', 'irrational', 'interpretations', 'ality', 'simms'] ['negate'] ['religion', 'religious', 'expressions', 'study'] ['people', 'appeal', 'religious', 'entities', 'during', 'moments'] ['hardship'] ['religion'] ['contagion', 'these', 'categories', 'study'] ['perception', 'diseases', 'cancer', 'support', 'groups', 'people'] ['affected', 'eating', 'disorder', 'alcoholics', 'alpers'] ['kornfield'] [] [] ['moralization'] ['avoidance'] ['perception'] ['category', 'model', 'perception', 'connected'] ['epidemics'] [] ['polarization', 'segregation', 'different', 'personal', 'pronouns', 'study', 'group', 'dynamics', 'characterize', 'language'] ['markers', 'racism', 'arguello', 'personal'] ['pronouns', 'markers', 'differentiation'] ['considered', 'studies', 'racist', 'language', 'figea'] [] [] [] [] ['differ'] ['stigmatization', 'blame'] ['abuse'] ['pronouns', 'quantify', 'blame'] ['personal', 'borelli', 'sbarra', 'political', 'windsor', 'speech', 'associated'] ['oriented', 'statements', 'elsherief'] ['categories'] ['previous'] ['cooperation'] ['coordination', 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'minimum', 'maximum', 'values', 'during', 'whole'] ['period'] [] [] [] ['mentions', 'medical', 'entities', 'state', 'learning', 'method', 'medical', 'entity', 'extraction'] ['identify', 'medical', 'symptoms', 'twitter', 'relation', 'covid', 'scepanovic', 'applied', 'tweets'] ['method', 'extracts', 'grams', 'representing', 'medical', 'symptoms', 'feeling', 'method', 'based'] ['sequence', 'tagging', 'architecture', 'huang', 'combination', 'glove', 'embeddings', 'nington', 'roberta', 'contextual', 'embeddings', 'optimize', 'entity', 'extraction'] ['performance', 'noisy', 'textual', 'social', 'media', 'trained', 'sequence', 'tagging', 'architecture', 'micromed'] ['database', 'jimeno', 'yepes', 'collection', 'tweets', 'manually', 'labeled', 'medical', 'entities', 'hyper', 'parameters', 'hidden', 'units', 'batch', 'learning', 'which', 'gradually', 'halved'] ['whenever', 'there', 'performance', 'improvement', 'after', 'epochs', 'trained', 'maximum', 'epochs'] ['before', 'learning', 'became', 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'visited', 'response', 'covid', 'crisis', 'foursquare', 'publicly'] ['available', 'gathered', 'million', 'users', 'these', 'users', 'always', 'during', 'period'] ['collection', 'meaning', 'allowed', 'application', 'gather', 'location', 'times'] ['application', 'published', 'through', 'visitdata', 'website', 'consists', 'daily', 'number'] ['users', 'visiting', 'venue', 'state', 'starting', 'february', 'present'] ['users', 'visited', 'schools', 'indiana', 'february', 'overall', 'distinct', 'location', 'categories', 'provided', 'obtain'] ['country', 'temporal', 'indicators', 'first', 'applied', 'normalization', 'values'] [] [] [] ['averaged', 'values', 'across', 'states'] [] [] [] [] [] ['where', 'total', 'number', 'states', 'weighting', 'state', 'equally', 'obtained', 'measure'] ['representative', 'whole', 'territory', 'rather', 'being', 'biased', 'towards', 'density', 'regions', 'epidemic', 'psychology', 'works', 'twitter'] ['series', 'smoothing', 'temporal', 'indicators', 'affected', 'large', 'fluctuations', 'extract'] ['consistent', 'trends', 'series', 'applied', 'smoothing', 'function', 'common', 'practice', 'analyzing'] ['temporal', 'extracted', 'social', 'media', 'connor', 'given', 'varying', 'signal', 'apply'] ['boxcar', 'moving', 'average', 'window', 'previous'] [] [] [] [] [] [] [] [] ['selected', 'window', 'weekly', 'windows', 'typically', 'smooth'] ['variations', 'weekly', 'periodicities', 'connor', 'applied', 'smoothing', 'series'] ['language', 'categories'] [] [] ['mentions', 'medical', 'entities'] [] [] ['interaction', 'types'] [] [] [] ['foursquare', 'visits'] [] [] [] ['change', 'point', 'detection', 'identify', 'phases', 'characterized', 'different', 'combinations', 'language', 'categories'] ['identified', 'change', 'points', 'periods', 'which', 'values', 'categories', 'varied', 'considerably', 'quantify'] ['variations', 'language', 'category', 'computed'] [] [] ['namely', 'daily', 'average', 'squared', 'gradi', 'utkepohl', 'smoothed', 'standardized', 'fractions', 'category', 'calculate', 'gradient'] ['python', 'function', 'numpy', 'gradient', 'gradient', 'provides', 'measure', 'increase', 'decrease'] ['signal', 'consider', 'absolute', 'value', 'gradient', 'account', 'magnitude', 'change', 'rather', 'direc', 'change', 'identify', 'periods', 'consistent', 'change', 'opposed', 'quick', 'instantaneous', 'shifts', 'apply', 'temporal'] ['smoothing', 'equation', 'series', 'gradients', 'denote', 'smoothed', 'squared', 'gradients'] [] ['average', 'gradients', 'language', 'categories', 'obtain', 'overall', 'gradient'] [] [] [] [] [] [] [] [] [] ['peaks', 'series', 'represent', 'highest', 'variation', 'marked', 'change', 'points', 'using'] ['python', 'function', 'scipy', 'signal', 'peaks', 'identified', 'peaks', 'local', 'maxima', 'whose', 'values', 'higher'] ['average', 'standard', 'deviations', 'common', 'practice', 'palshikar'] ['results'] ['language', 'until', 'first', 'contagion', 'during', 'first', 'residents', 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'different', 'language', 'categories', 'their', 'standardized', 'fractions', 'reached', 'maximum'] ['reporting', 'percentage', 'increase', 'increase', 'compared', 'average', 'february', 'april'] [] ['denoted', 'table', 'first', 'phase', 'refusal', 'phase', 'characterized', 'anxiety'] ['death', 'frequently', 'mentioned', 'february', 'compared', 'average', 'during'] ['whole', 'period', 'pronoun', 'temporal', 'state', 'average', 'suggests'] ['focus', 'discussion', 'implications', 'viral', 'epidemic', 'others', 'infection'] ['discovered', 'other', 'language', 'categories', 'exhibited', 'significant', 'variations', 'which', 'reflected'] ['overall', 'situation', 'business', 'usual'] ['second', 'phase', 'anger', 'phase', 'began', 'february', 'outburst', 'negative', 'emotions', 'predominantly'] ['anger', 'right', 'after', 'first', 'covid', 'contagion', 'announced', 'abstract', 'death', 'replaced'] ['expressions', 'concrete', 'health', 'concerns', 'words', 'expressing', 'mentions', 'parts'] 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['alcohol', 'exercising', 'economic'] [] [] [] ['interactions'] [] ['support', 'power', 'conflict'] [] [] [] ['medical'] [] ['physical', 'health', 'mental', 'health'] [] [] [] [] ['mobility'] [] ['first'] [] [] [] ['grocery', 'travel', 'transport', 'outdoors', 'recreation'] [] [] [] [] ['figure', 'epidemic', 'psychology', 'twitter', 'during', 'first', 'contagion', 'evolution'] ['different', 'language', 'categories', 'tweets', 'related', 'covid', 'heatmaps', 'represents'] ['language', 'category', 'words', 'expressing', 'anxiety', 'manual', 'coding', 'associated', 'three'] ['psycho', 'social', 'epidemics', 'color', 'represents', 'daily', 'standardized', 'fraction', 'people', 'words', 'related'] ['category', 'values', 'higher', 'average', 'those', 'lower', 'categories'] ['partitioned', 'three', 'groups', 'according', 'psycho', 'social', 'epidemics', 'model', 'morality', 'action'] ['average', 'gradient', 'instantaneous', 'variation', 'language', 'categories', 'peaks', 'gradient', 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['activities', 'business', 'usual'] ['anger', 'phase', 'discussion', 'characterized', 'outrage', 'against', 'three', 'categories', 'foreigners', 'especially'] ['chinese', 'individuals', 'supplementary', 'materials', 'detail', 'political', 'opponents', 'people', 'adopted'] ['different', 'behavioral', 'responses', 'outbreak', 'level', 'conflict', 'corroborates', 'strong', 'postulate'] ['against', 'other', 'science', 'religion', 'prominent', 'topics', 'discussion', 'lively', 'debate', 'raged'] ['around', 'validity', 'scientists', 'recommendations', 'social', 'groups', 'their', 'hopes', 'rather'] ['science', 'mentions', 'people', 'isolating', 'became', 'frequent', 'highlighted', 'contrast'] ['between', 'judicious', 'individuals', 'careless', 'crowds'] ['finally', 'during', 'acceptance', 'phase', 'outburst', 'anger', 'sorrow', 'caused', 'mourning'] ['thousands', 'people', 'accepting', 'threat', 'virus', 'twitter', 'users'] ['collective', 'solutions', 'problem', 'overcome', 'although', 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'framework'] ['state', 'proceedings', 'conference', 'north', 'american', 'chapter', 'association'] ['computational', 'linguistics', 'pages'] ['alpers', 'winzelberg', 'classen', 'roberts', 'koopman', 'taylor', 'evaluation'] ['computerized', 'analysis', 'internet', 'breast', 'cancer', 'support', 'group', 'computers', 'human', 'behavior'] [] ['arguello', 'butler', 'joyce', 'kraut', 'foundations'] ['successful', 'individual', 'group', 'interactions', 'online', 'communities', 'proceedings', 'sigchi', 'conference'] ['human', 'factors', 'computing', 'systems', 'pages'] ['bagnoli', 'sguanci', 'perception', 'epidemic', 'modeling', 'physical', 'review', '061904'] ['bansal', 'chowell', 'simonsen', 'vespignani', 'viboud', 'infectious', 'disease', 'surveillance'] ['modeling', 'journal', 'infectious', 'diseases', 'suppl'] ['bareket', 'bojmel', 'shahar', 'margalit', 'covid', 'related', 'economic', 'anxiety', 'health', 'anxiety'] ['findings', 'israel', 'international', 'journal', 'cognitive', 'therapy'] ['baronchelli', 'emergence', 'consensus', 'primer', 'royal', 'society', 'science', '172189'] ['bauch', 'galvani', 'social', 'factors', 'epidemiology', 'science'] ['bento', 'nguyen', 'lozano', 'rojas', 'simon', 'evidence', 'internet', 'search'] ['shows', 'information', 'seeking', 'responses', 'local', 'covid', 'cases', 'proceedings', 'national', 'academy'] ['sciences', 'epidemic', 'psychology', 'works', 'twitter'] ['bessi', 'ferrara', 'social', 'distort', 'presidential', 'election', 'online', 'discussion', 'first', 'monday'] [] ['exchange', 'power', 'social', 'transaction', 'publishers'] ['bleidorn', 'klimstra', 'denissen', 'rentfrow', 'potter', 'gosling', 'personality', 'maturation'] ['around', 'world', 'cross', 'cultural', 'examination', 'social', 'investment', 'theory', 'psychological', 'science'] [] ['borelli', 'sbarra', 'trauma', 'history', 'linguistic', 'focus', 'moderate', 'course', 'psychological'] ['adjustment', 'divorce', 'journal', 'social', 'clinical', 'psychology'] ['braun', 'clarke', 'using', 'thematic', 'analysis', 'psychology', 'qualitative', 'research', 'psychology'] [] ['broniatowski', 'jamison', 'alkulaib', 'benton', 'quinn', 'dredze'] ['weaponized', 'health', 'communication', 'twitter', 'russian', 'trolls', 'amplify', 'vaccine', 'debate', 'american', 'journal'] ['public', 'health'] ['brooks', 'webster', 'smith', 'woodland', 'wessely', 'greenberg', 'rubin'] ['psychological', 'impact', 'quarantine', 'reduce', 'rapid', 'review', 'evidence', 'lancet'] ['language', 'communicating', 'threat', 'public', 'discourse', 'springer'] ['lerman', 'ferrara', 'tracking', 'social', 'media', 'discourse', 'about', 'covid', 'pandemic', 'development'] ['public', 'coronavirus', 'twitter', 'public', 'health', 'surveillance', 'e19273'] ['19273'] ['eysenbach', 'pandemics', 'twitter', 'content', 'analysis', 'tweets', 'during'] ['outbreak'] ['aiello', 'varga', 'quercia', 'social', 'dimensions', 'conversations', 'relationships'] ['proceedings', 'conference'] ['cinelli', 'quattrociocchi', 'galeazzi', 'valensise', 'brugnoli', 'schmidt', 'zollo'] ['scala', 'covid', 'social', 'media', 'infodemic', 'arxiv', 'preprint', 'arxiv', '05004'] ['coppersmith', 'dredze', 'harman', 'quantifying', 'mental', 'health', 'signals', 'twitter', 'proceedings'] ['workshop', 'computational', 'linguistics', 'clinical', 'psychology', 'linguistic', 'signal', 'clinical', 'reality', 'pages'] [] ['schiano', 'covid', 'hangover', 'rising', 'alcohol', 'disorder', 'alcohol', 'associated'] ['liver', 'disease', 'hepatology'] ['rappaz', 'aiello', 'quercia', 'coloring', 'links', 'capturing', 'social', 'perceived'] ['proceedings', 'conference', 'computer', 'supported', 'cooperative', 'social', 'computing'] ['pages'] ['deutsch', 'trust', 'suspicion', 'journal', 'conflict', 'resolution'] ['dredze', 'bergsma', 'carmen', 'twitter', 'geolocation', 'system', 'applications', 'public'] ['health', 'workshops', 'twenty', 'seventh', 'conference', 'artificial', 'intelligence'] ['dutheil', 'mondillon', 'navel', 'second', 'tsunami', 'pandemic', 'psychological'] ['medicine', 'pages'] ['elsherief', 'kulkarni', 'nguyen', 'belding', 'lingo', 'target', 'based', 'linguistic', 'analysis'] ['speech', 'social', 'media', 'twelfth', 'international', 'conference', 'social', 'media'] ['engle', 'stromme', 'staying', 'mobility', 'effects', 'covid', 'available'] ['ferrara', 'covid', 'twitter', 'conspiracies', 'social', 'media', 'activism', 'arxiv', 'preprint', 'arxiv', '09531'] [] ['ferrara', 'varol', 'davis', 'menczer', 'flammini', 'social', 'communications'] ['epidemic', 'psychology', 'works', 'twitter'] ['fetzer', 'hensel', 'hermle', 'coronavirus', 'perceptions', 'economic', 'anxiety', 'review', 'economics'] ['statistics', 'pages'] ['figea', 'kaati', 'scrivens', 'measuring', 'online', 'affects', 'white', 'supremacy', 'forum', 'conference'] ['intelligence', 'security', 'informatics', 'pages'] ['finlay', 'gilmore', 'covid', 'alcohol', 'dangerous', 'cocktail'] ['fiske', 'cuddy', 'glick', 'universal', 'dimensions', 'social', 'cognition', 'warmth', 'competence', 'trends'] ['cognitive', 'sciences'] ['frimer', 'schaefer', 'oakes', 'moral', 'actor', 'selfish', 'agent', 'journal', 'personality', 'social', 'psychology'] [] ['liang', 'saroha', 'people', 'react', 'virus', 'outbreaks'] ['twitter', 'computational', 'content', 'analysis', 'american', 'journal', 'infection', 'control'] ['galea', 'merchant', 'lurie', 'mental', 'health', 'consequences', 'covid', 'physical', 'distancing'] ['prevention', 'early', 'intervention', 'internal', 'medicine'] ['liang', 'kruse', 'mapping', 'county', 'level', 'mobility', 'pattern', 'changes', 'united'] ['states', 'response', 'covid', 'sigspatial', 'special'] ['gibbs', 'thematic', 'coding', 'categorizing', 'analyzing', 'qualitative', 'london', 'pages'] ['french', 'gergle', 'oberlander', 'language', 'emotion', 'short', 'texts', 'proceedings'] ['conference', 'computer', 'supported', 'cooperative', 'pages'] ['goffman', 'stigma', 'notes', 'management', 'spoiled', 'identity', 'simon', 'schuster'] ['gonzalez', 'hidalgo', 'barabasi', 'understanding', 'individual', 'human', 'mobility', 'patterns', 'nature'] [] ['graham', 'haidt', 'nosek', 'liberals', 'conservatives', 'different', 'moral', 'foundations'] ['journal', 'personality', 'social', 'psychology'] ['graham', 'haidt', 'koleva', 'motyl', 'wojcik', 'ditto', 'moral', 'foundations', 'theory'] ['pragmatic', 'validity', 'moral', 'pluralism', 'advances', 'experimental', 'social', 'psychology', 'volume', 'pages'] ['elsevier'] ['grossman', 'rexer', 'thirumurthy', 'political', 'partisanship', 'influences', 'behavioral', 'responses'] ['governors', 'recommendations', 'covid', 'prevention', 'united', 'states', 'available', '3578695'] ['hebert', 'clemow', 'pbert', 'ockene', 'ockene', 'social', 'desirability', 'dietary', 'report'] ['compromise', 'validity', 'dietary', 'intake', 'measures', 'international', 'journal', 'epidemiology'] ['hochreiter', 'schmidhuber', 'short', 'memory', 'neural', 'computation'] ['hofstede', 'hofstede', 'minkov', 'cultures', 'organizations', 'software', 'volume'] ['mcgraw'] ['holmes', 'connor', 'perry', 'tracey', 'wessely', 'arseneault', 'ballard', 'christensen'] ['silver', 'everall', 'multidisciplinary', 'research', 'priorities', 'covid', 'pandemic', 'action', 'mental'] ['health', 'science', 'lancet', 'psychiatry'] ['jiang', 'assessment', 'public', 'attention', 'perception', 'emotional'] ['behavioural', 'responses', 'covid', 'outbreak', 'social', 'media', 'surveillance', 'china', 'perception', 'emotional'] ['behavioural', 'responses', 'covid', 'outbreak', 'social', 'media', 'surveillance', 'china'] ['huang', 'bidirectional', 'models', 'sequence', 'tagging', 'arxiv', 'preprint', 'arxiv', '01991'] [] ['auriemma', 'cashaw', 'report', 'underreporting', 'depression', 'journal'] ['personality', 'assessment', 'epidemic', 'psychology', 'works', 'twitter'] ['husnayain', 'applications', 'google', 'search', 'trends', 'communication', 'infectious'] ['disease', 'management', 'study', 'covid', 'outbreak', 'taiwan', 'international', 'journal', 'infectious', 'diseases'] [] ['jimeno', 'yepes', 'mackinlay', 'identifying', 'diseases', 'drugs', 'symptoms', 'twitter'] ['studies', 'health', 'technology', 'informatics'] ['johnson', 'fendrich', 'modeling', 'sources', 'report', 'survey', 'epidemiology', 'annals'] ['epidemiology'] ['tobin', 'massey', 'anderson', 'measuring', 'emotional', 'expression', 'linguistic'] ['inquiry', 'count', 'american', 'journal', 'psychology', 'pages'] ['huremovi', 'psychology', 'pandemic', 'psychiatry', 'pandemics', 'pages', 'springer'] ['kornfield', 'gustafson', 'before', 'relapse'] ['language', 'online', 'discussion', 'forum', 'predicts', 'risky', 'drinking', 'among', 'those', 'recovery', 'health'] ['communication'] ['kouzy', 'jaoude', 'kraitem', 'karam', 'zarka', 'traboulsi'] ['baddour', 'coronavirus', 'viral', 'quantifying', 'covid', 'misinformation', 'epidemic', 'twitter', 'cureus'] [] ['ubler', 'wessler', 'avioli', 'death', 'dying'] ['goodchild', 'spatial', 'temporal', 'socioeconomic', 'patterns', 'twitter', 'flickr'] ['cartography', 'geographic', 'information', 'science'] ['impact', 'covid', 'epidemic', 'declaration', 'psychological'] ['consequences', 'study', 'active', 'weibo', 'users', 'international', 'journal', 'environmental', 'research', 'public', 'health'] [] ['liang', 'effect', 'covid', 'youth', 'mental', 'health'] ['psychiatric', 'quarterly', 'pages'] ['goyal', 'joshi', 'lewis', 'zettlemoyer', 'stoyanov', 'roberta'] ['robustly', 'optimized', 'pretraining', 'approach', 'arxiv', 'preprint', 'arxiv', '11692'] ['utkepohl', 'introduction', 'multiple', 'series', 'analysis', 'springer', 'science', 'business', 'media'] ['maslow', 'theory', 'human', 'motivation', 'psychological', 'review'] ['mcconnell', 'banks', 'avian', 'planning', 'possible', 'pandemic', 'trading', 'technology'] ['mohammad', 'turney', 'crowdsourcing', 'emotion', 'association', 'lexicon', 'computational', 'intelligence'] [] ['moinet', 'pastor', 'satorras', 'barrat', 'effect', 'perception', 'epidemic', 'spreading', 'temporal', 'networks'] ['physical', 'review', '012313'] ['connor', 'balasubramanyan', 'routledge', 'smith', 'tweets', 'polls', 'linking', 'sentiment'] ['public', 'opinion', 'series', 'fourth', 'international', 'conference', 'weblogs', 'social', 'media'] ['oyeyemi', 'gabarron', 'ebola', 'twitter', 'misinformation', 'dangerous', 'combination'] ['g6178'] ['painter', 'political', 'beliefs', 'affect', 'compliance', 'covid', 'social', 'distancing', 'orders', 'available'] ['3569098'] ['palshikar', 'simple', 'algorithms', 'detection', 'series', 'advanced'] ['analysis', 'business', 'analytics', 'intelligence', 'volume'] ['paszke', 'gross', 'chintala', 'chanan', 'devito', 'desmaison', 'antiga', 'lerer'] ['automatic', 'differentiation', 'pytorch', 'proceedings', 'advances', 'neural', 'information', 'processing', 'systems'] ['autodiff', 'workshop', 'epidemic', 'psychology', 'works', 'twitter'] ['pennington', 'socher', 'manning', 'glove', 'global', 'vectors', 'representation', 'proceedings'] ['conference', 'empirical', 'methods', 'natural', 'language', 'processing', 'pages', 'association', 'computational'] ['linguistics'] ['perrin', 'anderson', 'share', 'adults', 'using', 'social', 'media', 'including', 'facebook'] ['mostly', 'unchanged', 'since', 'https', 'pewresearch'] ['share', 'adults', 'using', 'social', 'media', 'including', 'facebook', 'mostly', 'unchanged', 'since'] ['plutchik', 'emotions', 'university', 'press', 'america'] ['pulido', 'villarejo', 'carballido', 'redondo', 'covid', 'infodemic', 'retweets'] ['science', 'based', 'information', 'coronavirus', 'false', 'information', 'international', 'sociology'] ['0268580920914755'] ['nationwide', 'survey', 'psychological', 'distress', 'among', 'chinese'] ['people', 'covid', 'epidemic', 'implications', 'policy', 'recommendations', 'general', 'psychiatry'] ['rezapour', 'diesner', 'enhancing', 'measurement', 'social', 'effects', 'capturing', 'morality'] ['proceedings', 'tenth', 'workshop', 'computational', 'approaches', 'subjectivity', 'sentiment', 'social', 'media'] ['analysis', 'pages'] ['russell', 'mining', 'social', 'mining', 'facebook', 'twitter', 'linkedin', 'google', 'github'] ['reilly', 'media'] ['alani', 'alleviating', 'sparsity', 'twitter', 'sentiment', 'analysis', 'workshop', 'proceedings'] [] ['salathe', 'bengtsson', 'bodnar', 'brewer', 'brownstein', 'buckee', 'campbell', 'cattuto'] ['khandelwal', 'mabry', 'digital', 'epidemiology', 'comput', 'e1002616'] ['scepanovic', 'martin', 'lopez', 'quercia', 'baykaner', 'extracting', 'medical', 'entities', 'social', 'media'] ['proceedings', 'conference', 'health', 'inference', 'learning', 'pages'] ['schutz', 'luckmann', 'zaner', 'engelhardt', 'structures', 'world', 'number', 'northwest', 'university', 'studies', 'phenomenology', 'existential', 'philosophy', 'northwestern', 'university', 'press'] ['9780810106222', 'https', 'books', 'google', 'books', 'lgxbxi0xsh8c'] ['gustafson', 'hawkins', 'cleary', 'mctavish', 'pingree', 'eliason'] ['lumpkins', 'effects', 'prayer', 'religious', 'expression', 'within', 'computer', 'support', 'groups', 'women', 'breast'] ['cancer', 'psycho', 'oncology', 'journal', 'psychological', 'social', 'behavioral', 'dimensions', 'cancer'] [] ['rudzicz', 'detecting', 'anxiety', 'through', 'reddit', 'proceedings', 'fourth', 'workshop', 'compu', 'tational', 'linguistics', 'clinical', 'psychology', 'linguistic', 'signal', 'clinical', 'reality', 'pages'] ['shultz', 'baingana', 'neria', 'ebola', 'outbreak', 'mental', 'health', 'current', 'status', 'recom', 'mended', 'response'] ['simms', 'ramstedt', 'richards', 'martinez', 'giraud', 'carrier', 'detecting', 'cognitive', 'distortions'] ['through', 'machine', 'learning', 'analytics', 'international', 'conference', 'healthcare', 'informatics'] ['pages'] ['smith', 'shinebourne', 'interpretative', 'phenomenological', 'analysis', 'american', 'psychological', 'association'] [] ['sommariva', 'vamos', 'mantzarlis', 'martinez', 'tyson', 'spreading', 'exploring'] ['health', 'messages', 'social', 'media', 'implications', 'health', 'professionals', 'using', 'study', 'american', 'journal'] ['health', 'education'] ['strong', 'epidemic', 'psychology', 'model', 'sociology', 'health', 'illness'] ['tajfel', 'turner', 'austin', 'worchel', 'integrative', 'theory', 'intergroup', 'conflict', 'organizational'] ['identity'] ['tausczik', 'pennebaker', 'psychological', 'meaning', 'words', 'computerized', 'analysis'] ['methods', 'journal', 'language', 'social', 'psychology', 'epidemic', 'psychology', 'works', 'twitter'] ['economist', 'millions', 'stuck', 'online', 'wellness', 'dustry', 'booming', 'https', 'economist', 'international'] ['millions', 'stuck', 'online', 'wellness', 'industry', 'booming'] ['bavel', 'baicker', 'boggio', 'capraro', 'cichocka', 'cikara', 'crockett'] ['douglas', 'druckman', 'using', 'social', 'behavioural', 'science', 'support', 'covid', 'pandemic', 'response'] ['nature', 'human', 'behaviour', 'pages'] ['varol', 'ferrara', 'davis', 'menczer', 'flammini', 'online', 'human', 'interactions', 'detection'] ['estimation', 'characterization', 'eleventh', 'international', 'conference', 'social', 'media'] ['immediate', 'psychological', 'responses', 'associated'] ['factors', 'during', 'initial', 'stage', 'coronavirus', 'disease', 'covid', 'epidemic', 'among', 'general', 'population'] ['china', 'international', 'journal', 'environmental', 'research', 'public', 'health'] ['waterloo', 'baumgartner', 'peter', 'valkenburg', 'norms', 'online', 'expressions', 'emotion'] ['comparing', 'facebook', 'twitter', 'instagram', 'whatsapp', 'media', 'society'] ['windsor', 'dowell', 'graesser', 'language', 'autocrats', 'leaders', 'language', 'natural', 'disaster', 'crises'] ['hazards', 'crisis', 'public', 'policy'] ['wojcik', 'hughes', 'sizing', 'twitter', 'users', 'washington', 'research', 'center'] ['theis', 'kordy', 'language', 'eating', 'disorder', 'blogs', 'psychological', 'implications', 'social', 'online'] ['activity', 'journal', 'language', 'social', 'psychology'] ['propagating', 'debunking', 'conspiracy', 'theories', 'twitter', 'during', 'virus', 'outbreak'] ['cyberpsychology', 'behavior', 'social', 'networking'] ['torres', 'menczer', 'prevalence', 'credibility', 'information', 'twitter', 'during'] ['covid', 'outbreak', 'arxiv', 'preprint', 'arxiv', '14484'] ['acknowledgments'] ['thank', 'sarah', 'konrath', 'rosta', 'farzan', 'licia', 'capra', 'their', 'useful', 'feedback', 'manuscript', 'research'] ['partly', 'supported', 'grant', 'green', 'routes', '869764'] ['availability'] ['daily', 'aggregates', 'measurements', 'available', 'https', 'figshare', '14892642'] ['tweet', 'available', 'https', 'github', 'echen102', 'covid', 'tweetids', 'other', 'datasets'] ['visualizations', 'available', 'project', 'social', 'dynamics', 'epidemicpsychology'] ['competing', 'interests'] ['author', 'declare', 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'miyamae', 'asami', 'komazawa', 'chika', 'yokoyama', 'masaya'] ['national', 'center', 'neurology', 'psychiatry', 'tokyo', 'japan'] ['paper', 'discusses', 'positive', 'emotions', 'maintain', 'improve', 'mental', 'health', 'during'] ['covid', 'outbreak', 'taking', 'account', 'examples', 'social', 'interaction', 'positive', 'psychology', 'research'] ['efforts', 'japanese', 'context'] ['keywords', 'positive', 'emotion', 'covid', 'pandemic', 'strength'] ['uncontrollable', 'spread', 'coronavirus', 'covid'] ['promulgating', 'sense', 'hopelessness'] ['there', 'seems', 'longer', 'exist', 'place', 'anywhere'] ['world', 'mental', 'health', 'crises', 'occurring', 'worldwide', 'affecting'] ['individuals', 'variety', 'including', 'stress', 'anxiety'] ['helplessness', 'depression', 'although', 'number'] ['covid', 'infections', 'mortality', 'rates', 'japan', 'mained', 'relatively', 'compared', 'those', 'western', 'tries', 'country', 'still', 'explosive', 'spread'] ['infection', 'there', 'rising', 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'group', 'briley'] ['gerbner', 'decline'] ['social', 'diversity', 'likely', 'prominent', 'regions'] ['ethnically', 'diverse', 'begin', 'including'] ['south', 'korea'] ['decreases', 'salience', 'group', 'boundaries', 'sible', 'given', 'communications', 'during'] ['pandemic', 'conducted', 'online', 'physical', 'location'] ['currently', 'little', 'influence', 'people', 'about', 'doing'] ['attending', 'workshops', 'connecting', 'others'] ['therefore', 'during', 'pandemic', 'people'] ['increasingly', 'relied', 'individuating', 'characteristics'] ['personality', 'evaluating', 'interacting', 'others'] ['correspondingly', 'characteristics', 'associated'] ['their', 'physical', 'location', 'nationality'] ['ethnicity', 'indeed', 'focusing', 'individuating', 'informa', 'shown', 'reduce', 'stereotyping', 'implicit'] ['explicit', 'person', 'perception', 'rubinstein'] ['however', 'whether', 'indeed', 'pandemic', 'magnified'] ['minimised', 'perceived', 'differences', 'among', 'groups'] 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'considered', 'surprised', 'realize'] ['delusional', 'delirium', 'makes', 'believe'] ['avoid', 'considering', 'arguments', 'concerning', 'death', 'everyday'] ['suddenly', 'realized', 'procrastinate', 'moment'] ['encountering', 'topic', 'necessary'] ['unprepared', 'exactly', 'happening'] ['forced', 'anguish', 'death', 'without'] ['having', 'tools'] ['evidence', 'alessandra', 'gioele', 'years'] ['tested', 'people', 'avoid', 'talking', 'about', 'death'] ['dying', 'useful', 'interesting', 'because', 'within', 'group'] ['friends', 'opportunity', 'again'] ['dealt', 'topic', 'covid', 'topic', 'death'] ['block', 'practical', 'point'] ['about', 'death'] ['similarly', 'giulia', 'years', 'expressed'] ['course', 'helped', 'understand', 'importance', 'actively', 'talking'] ['about', 'death', 'understanding', 'importance', 'talking', 'about'] ['death', 'given', 'opportunity', 'experience', 'firsthand'] ['beneficial', 'constructive', 'dialogue', 'death', 'death'] ['perhaps', 'current', 'pandemic', 'could', 'managed'] ['different', 'distressing', 'years', 'recognized'] ['given', 'course', 'referred', 'directly'] ['personal', 'experience', 'saying', 'noticed', 'decrease'] ['tendency', 'change', 'subject', 'someone', 'communicated'] ['grieving', 'situation', 'these', 'considerations'] ['roberta', 'years', 'concluded', 'saying', 'hoped'] ['experience', 'pandemic', 'would', 'community'] ['removal', 'concept', 'dying', 'important', 'thing'] ['compared', 'terrible', 'experience'] ['covid', 'there', 'social', 'removal', 'would'] ['important', 'process', 'meaningful'] ['indeed', 'suddenly', 'understood', 'removal'] ['avoidance', 'themes', 'remind', 'finitude'] ['impact', 'death', 'avoided'] ['years', 'reported', 'thought', 'similar'] ['roberta', 'saying', 'course', 'really'] ['awareness', 'death', 'actually', 'natural'] ['should', 'therefore', 'demonized', 'avoided', 'theme'] ['believe', 'understood', 'thanks', 'course'] ['attended', 'exactly', 'during', 'pandemic', 'awareness', 'death'] ['happen', 'though', 'expect'] ['should', 'learn', 'serenely', 'accept'] ['though', 'fearing', 'unfortunately', 'social', 'cultural'] ['background'] ['similarly', 'alberto', 'years', 'reflected', 'society'] ['difficult', 'relate', 'theme', 'death'] ['through', 'pandemic', 'allowed', 'reflect'] ['relationship', 'between', 'death', 'western', 'society'] ['society', 'accustomed', 'close'] ['intimate', 'natural', 'human', 'being', 'possesses'] ['awareness', 'finitude'] ['slightly', 'different', 'point', 'issue', 'brought'] ['alice', 'years', 'instead', 'considered', 'professional'] ['aspect', 'importance', 'manage', 'healthcare'] ['professional', 'theme', 'death', 'patients', 'without', 'feeling'] ['overwhelmed', 'realized', 'importance', 'beginning', 'speak'] ['honestly', 'about', 'death', 'confusion', 'there', 'around', 'these'] ['issues', 'terms', 'unconscious', 'beliefs', 'representations'] ['terms', 'misinformation', 'clearer'] ['importance', 'information', 'especially'] ['those', 'those'] ['second', 'prevalent', 'thematic'] ['importance', 'community', 'ritual'] ['funeral', 'spirituality'] ['significantly', 'linked', 'first', 'thematic', 'second'] ['highlighted', 'further', 'discovery', 'importance'] ['accompanying', 'funeral', 'those'] ['community', 'indeed', 'condition', 'constant', 'social'] ['cultural', 'removal', 'death', 'devastating', 'effects', 'during'] ['period', 'mortality', 'salience', 'pandemic', 'brought'] ['participants', 'acknowledge', 'vital', 'psychological', 'social'] ['function', 'funeral', 'rites', 'possess', 'since', 'mourners'] ['embraced', 'their', 'community', 'better', 'elaborate', 'their', 'grief'] ['expressed', 'participants', 'example', 'luisa'] ['years', 'thought', 'about', 'those'] ['hospital', 'great', 'relief', 'being', 'share'] ['others', 'through', 'internet', 'allowed'] ['alone', 'similarly', 'maria', 'woman', 'years'] ['participant', 'significantly', 'different'] ['group', 'highlighted', 'during', 'lockdown'] ['fundamental', 'resilient', 'maintain', 'contact'] ['though', 'physical', 'loved'] ['course', 'helped', 'realize', 'fundamental', 'social', 'relationships'] ['important', 'share', 'feelings', 'fears', 'others'] ['especially', 'periods', 'global', 'crisis', 'especially'] ['nurture', 'resilience', 'moment', 'regularly', 'having'] ['telephone', 'contacts', 'friends', 'relatives'] ['since', 'could', 'share', 'experience'] ['preparing', 'helped', 'purpose', 'could'] ['loved', 'though', 'distance'] ['authentic', 'supportive', 'relationships', 'voice', 'fears'] ['share'] ['giada', 'years', 'proceeded', 'direction'] ['specified', 'intensely'] ['thought', 'about', 'those', 'alone', 'suffer', 'during'] ['lockdown', 'committed', 'myself', 'intensely'] ['frontiers', 'public', 'health', 'frontiersin', 'april', 'volume', 'article', '625756testoni', 'psychology', 'students', 'perceptions', 'covid'] ['relationship', 'parents', 'together', 'looked'] ['moments', 'which', 'could', 'communicate', 'thought', 'about'] ['those', 'suffering', 'alone', 'costanza', 'years'] ['expressed', 'thoughts', 'thinking', 'about', 'those'] ['hospital', 'alone', 'understood', 'important'] ['community', 'attention', 'caused'] ['death', 'community', 'truly', 'never', 'makes'] ['alone', 'creates', 'network', 'support', 'sharing', 'ready'] ['support', 'never', 'considered'] ['important', 'aspect', 'social', 'before', 'monica', 'years'] ['highlighted', 'importance', 'support', 'network'] ['especially', 'delicate', 'phase'] ['communication', 'sharing', 'collective', 'community'] ['level', 'suffering', 'improve', 'grief', 'processing'] ['support', 'people', 'suffering', 'think'] ['about', 'before'] ['these', 'considerations', 'importance', 'sharing'] ['emotional', 'support', 'participants', 'consider'] ['fundamental', 'psychological', 'painful'] ['complex', 'situations', 'present', 'years'] ['example', 'therefore', 'realized', 'important'] ['profession', 'perform', 'psychological'] ['field', 'thought', 'about', 'support', 'given'] ['fundamental', 'receive', 'especially', 'situations'] ['living', 'agreeing', 'lucia', 'years'] ['before', 'course', 'never', 'grasped', 'positive', 'aspect'] ['being', 'dying', 'person', 'before', 'never'] ['thought', 'about', 'importance', 'human', 'aspect', 'health'] ['instead', 'understanding', 'relationships', 'become'] ['essential', 'tools'] ['according', 'participants', 'importance', 'relationships'] ['evident', 'moments', 'difficulty', 'pandemic'] ['support', 'community', 'becomes', 'crucial'] ['moment', 'mourning', 'members', 'gather', 'around'] ['mourners', 'costanza', 'struck', 'difference'] ['between', 'grief', 'mourning', 'passage', 'first'] ['second', 'thanks', 'funeral', 'closeness'] ['community', 'understood', 'fundamental', 'elements'] ['missing', 'important', 'problems', 'arise'] ['reason', 'believe', 'should', 'support', 'those', 'their'] ['loved', 'ambulance', 'hospital'] [] ['funeral', 'ritual', 'meant', 'suffering'] ['people', 'mourn', 'vanessa', 'years', 'commented'] ['function', 'funeral', 'point', 'collective'] ['support', 'thanks', 'course', 'understood'] ['importance', 'funeral', 'ritual', 'closeness', 'support'] ['community', 'mourners', 'never'] ['asked', 'myself', 'question', 'rather', 'seemed', 'unimportant'] ['finally', 'understood', 'importance', 'meaning'] ['celebration', 'though', 'believer', 'agreeing'] ['lorenzo', 'years', 'think', 'course', 'helped'] ['above', 'words', 'something', 'already', 'sensed'] ['value', 'rituality', 'death', 'seeing'] ['impossible', 'rules', 'social', 'distancing'] ['anguish', 'dismay', 'anger', 'caused', 'population'] ['meaning', 'value', 'experience'] ['lastly', 'thematic', 'roberta', 'highlighted', 'another'] ['fundamental', 'aspect', 'difference', 'between', 'religiosity'] ['spirituality', 'which', 'allowed', 'freer', 'explore'] ['latter', 'personal', 'dimension', 'understanding'] ['spiritual', 'dimension', 'different', 'religiosity', 'revolves'] ['around', 'religion', 'finally', 'allowed', 'dialogue'] ['myself', 'reflect', 'deeply', 'coordinates', 'being'] ['pandemic', 'important'] ['third', 'prevalent', 'thematic'] ['significance', 'death', 'education', 'future'] ['health', 'professionals'] ['awareness', 'having', 'realistically', 'reckon', 'limit'] ['particular', 'limit', 'foreground'] ['pandemic', 'paralyzing', 'proper'] ['rites', 'support', 'mourners', 'those', 'moments', 'allowed'] ['participants', 'recognize', 'positive', 'aspects'] ['sharing', 'thoughts', 'opinions', 'feelings', 'within', 'educational', 'paths'] ['allow', 'place', 'death', 'historical', 'cultural', 'context', 'especially'] ['moments', 'extreme', 'uncertainly', 'present', 'covid'] ['related', 'participants', 'indeed', 'expressed', 'believed'] ['proper', 'death', 'education', 'paths', 'could', 'represent', 'valuable'] ['strengthen', 'people', 'psychological', 'resources'] ['these', 'complex', 'situations', 'example', 'according', 'alberto'] ['looking', 'death', 'tracing', 'historical', 'symbolic', 'cultural'] ['scientific', 'psychological', 'allowed', 'acquire', 'greater'] ['awareness', 'greater', 'critical', 'sense', 'about', 'events'] ['especially', 'during', 'acute', 'phase', 'pandemic', 'congested'] ['windows', 'media', 'specifically', 'course', 'death'] ['education', 'helped', 'participants', 'better', 'understand'] ['formulate', 'their', 'understanding', 'death'] ['example', 'chiara', 'years', 'expressed', 'study', 'themes'] ['related', 'death', 'dying', 'given', 'opportunity', 'better'] ['understand', 'death', 'understood', 'during', 'various'] ['become', 'aware', 'understand'] ['death', 'value', 'attribute', 'giving', 'opportunity'] ['reflect', 'relate', 'death', 'especially', 'sudden'] ['death', 'seems', 'helps', 'better', 'manage'] ['living', 'valeria', 'seemed', 'course', 'given'] ['tools', 'overcome', 'anguish', 'makes', 'death', 'fearful'] ['unthinkable', 'taught', 'theme'] ['entirety', 'without', 'taking', 'anything', 'granted'] ['relating', 'luigi', 'years', 'expressed'] ['course', 'helped', 'understand', 'pandemic', 'situation'] ['maturity', 'serene'] ['would', 'normally', 'certainly', 'opening'] ['scientific', 'point', 'topics', 'covered', 'course'] ['basis', 'reading', 'situation', 'maturity', 'awareness'] ['according', 'lucia', 'during', 'course', 'finally'] ['about', 'death', 'always', 'precisely'] ['period', 'given', 'shape', 'talking', 'about', 'death'] ['understand', 'origin', 'great', 'shortcomings'] ['there', 'society', 'according', 'participants'] ['being', 'understand', 'topics', 'magnitude', 'allows'] ['treat', 'objective', 'point'] ['emotional', 'involvement', 'example', 'elisa', 'years'] ['frontiers', 'public', 'health', 'frontiersin', 'april', 'volume', 'article', '625756testoni', 'psychology', 'students', 'perceptions', 'covid'] ['explained', 'helped', 'hearing', 'about', 'death'] ['critical', 'scholar', 'helped', 'topic'] ['normal', 'treatable', 'allowed', 'distance'] ['myself', 'death', 'inevitably', 'implies', 'positivity'] ['course', 'linked', 'allowed', 'focus'] ['recognize', 'importance', 'deserves', 'dealing'] ['these', 'issues', 'manuela', 'years', 'makes', 'possible'] ['relevance', 'meaning', 'significance', 'lived'] ['existence', 'rather', 'finitude', 'while'] ['similar', 'federica', 'years', 'expressed', 'herself', 'talking'] ['about', 'death', 'gives', 'meaning', 'makes', 'alive'] ['course', 'allowed', 'lockdown', 'valuing'] ['every', 'moment'] ['discussion'] ['present', 'study', 'which', 'summarized'] ['elaboration', 'results', 'figure', 'aimed', 'explore'] ['psychology', 'students', 'experiences', 'perspectives', 'concerning'] ['present', 'covid', 'related', 'mortality', 'salience', 'situation'] ['especially', 'lockdown', 'measures', 'light', 'death', 'education'] ['course', 'attended', 'concerning', 'theme', 'death'] ['international', 'studies', 'indeed', 'already', 'highlighted'] ['other', 'healthcare', 'students', 'especially', 'medical', 'nursing'] ['report', 'adequately', 'prepared', 'these', 'issues'] ['support', 'dying', 'patient', 'intense'] ['proper', 'death', 'education', 'palliative', 'trainings', 'indeed', 'still'] ['extremely'] ['current', 'covid', 'pandemic', 'exacerbated'] ['critical', 'issue', 'causing', 'intense', 'feelings', 'uncertainty'] ['distress', 'among', 'healthcare', 'students', 'generally'] ['unprepared', 'unprecedented', 'sanitary', 'crisis', 'extremely'] ['deathrates'] ['present', 'research', 'findings', 'highlighted', 'these', 'critical'] ['aspects', 'relation', 'psychology', 'students', 'concerning'] ['studies', 'conducted'] ['learn', 'covid', 'period'] ['related', 'abrupt', 'increase', 'mortality', 'salience', 'while', 'attending'] ['death', 'education', 'course', 'obtained', 'results', 'three'] ['forms', 'discovering', 'characterized', 'students', 'feeling'] ['learning', 'emerged', 'first', 'discovering', 'removal'] ['death', 'contemporary', 'culture', 'second', 'importance'] ['community', 'rituals', 'funeral', 'spirituality'] ['third', 'significance', 'death', 'education', 'future'] ['health', 'professionals'] ['respect', 'first', 'thematic', 'important'] ['feeling', 'students', 'experienced', 'become', 'aware', 'social'] ['strategies', 'characterize', 'removal', 'death'] ['consciousness', 'everyday', 'realizing'] ['general', 'inability', 'shocking', 'experience'] ['pandemic', 'paralyzing', 'adequate', 'skills', 'related'] ['death', 'exacerbated'] ['evident', 'pandemic', 'already', 'reported', 'other'] ['healthcare', 'students', 'literature', 'especially', 'undergraduate'] ['nurses', 'doctors', 'already', 'highlighted'] ['reported', 'overwhelmingly', 'unprepared', 'concerning'] ['present', 'sanitary', 'emergency', 'think', 'study'] ['contents', 'helped', 'participants', 'develop', 'broader'] ['perspective', 'problem', 'students', 'reported'] ['personal', 'experiences', 'specific', 'omitted'] ['report', 'because', 'could', 'participants', 'recognizable'] ['these', 'testimonies', 'described', 'important', 'losses', 'unanswered'] ['requests', 'needs', 'support', 'supporting', 'literature', 'addressed'] ['importance', 'reflecting', 'fundamental', 'existential', 'themes'] ['healthcare', 'students', 'order', 'acquire', 'broader', 'categories'] ['thought', 'suitable', 'language', 'meaning'] ['acquire', 'adequate', 'professional', 'skills'] ['these', 'themes', 'their', 'future'] ['second', 'thematic', 'highlighted', 'further', 'discovery'] ['significantly', 'related', 'first', 'importance'] ['accompanying', 'rites', 'those', 'funerals'] ['possible', 'attention', 'understand'] ['suffering', 'those', 'loved'] ['during', 'pandemic', 'without', 'being', 'accompany'] ['without', 'being', 'celebrate', 'appropriate'] ['funeral', 'ritual', 'other', 'literature', 'findings'] ['which', 'highlighted', 'healthcare', 'professionals', 'students'] ['trainees', 'general', 'painful', 'burden', 'having'] ['assist', 'support', 'patients', 'indeed', 'dying', 'alone'] ['support', 'family', 'members', 'could', 'properly', 'accompany'] ['later', 'mourn', 'their', 'loved', 'scenario'] ['reflection', 'spiritual', 'dimension', 'shape', 'until'] ['underestimated', 'linked', 'students', 'their'] ['closeness', 'others', 'sharing', 'negative', 'feelings'] ['caused', 'pandemic', 'since', 'forced', 'isolation'] ['isolated', 'moment', 'intense', 'therefore'] ['identified', 'themselves', 'those', 'hospitals', 'could'] ['contact', 'their', 'loved', 'course', 'therefore'] ['allowed', 'students', 'discover', 'importance', 'funeral'] ['spirituality', 'allowing', 'reconnect', 'explore', 'their'] ['inner', 'dimension', 'regardless', 'their', 'attitude', 'toward', 'religion'] ['other', 'studies', 'literature', 'indeed', 'highlighted'] ['essential', 'healthcare', 'students', 'believe', 'receiving', 'proper', 'training'] ['concerning', 'spiritual', 'aspects', 'patients'] ['contrary', 'current', 'information', 'reflection'] ['these', 'themes', 'healthcare', 'university', 'courses'] ['lastly', 'thematic', 'concerned', 'indeed', 'importance'] ['death', 'education', 'courses', 'university', 'students'] ['pursue', 'careers', 'healthcare', 'future', 'issues'] ['comprised', 'theme', 'acquisition', 'awareness'] ['personal', 'reflection', 'allow', 'recognition', 'control'] ['negative', 'emotions', 'death', 'dying', 'arouse', 'consequently'] ['allow', 'people', 'focus', 'attention', 'through'] ['dissolution', 'taboos', 'related', 'these', 'topics', 'become'] ['aware', 'learned', 'recognize', 'negative', 'emotions'] ['occur', 'situations', 'related', 'death', 'finally', 'positivity'] ['course', 'substantially', 'derived', 'focus'] ['recognition', 'importance', 'general', 'participants'] ['recognized', 'adequate', 'preparation', 'regarding'] ['topics', 'covered', 'course', 'better'] ['these', 'issues', 'serenely', 'their', 'future', 'health'] ['professionals', 'participants', 'therefore', 'expressed', 'positive'] ['psychological', 'educational', 'effects', 'valuing'] ['course', 'allowed', 'better', 'manage', 'social', 'trauma'] ['frontiers', 'public', 'health', 'frontiersin', 'april', 'volume', 'article', '625756testoni', 'psychology', 'students', 'perceptions', 'covid'] ['figure', 'findings', 'qualitative', 'analysis', 'themes', 'their', 'relationships'] ['caused', 'covid', 'appears', 'other'] ['literature', 'especially', 'studies', 'involving', 'other', 'healthcare'] ['students', 'which', 'confirmed', 'important'] ['implement', 'death', 'education', 'pathways', 'university', 'students'] ['especially', 'those', 'studying', 'become', 'health', 'professionals'] ['present', 'results', 'highlighted'] ['conclusions'] ['present', 'study', 'highlighted', 'death', 'education', 'course'] ['conducted', 'during', 'first', 'phase', 'covid', 'pandemic'] ['italy', 'helped', 'students', 'become', 'aware', 'issues', 'addressed'] ['through', 'contents', 'dialogues', 'encouraged', 'participants'] ['develop', 'their', 'critical', 'thinking', 'allowed', 'rediscover'] ['value', 'reflection', 'sense', 'limit', 'finitude'] ['different', 'representations', 'death', 'since', 'these', 'meanings'] ['usually', 'taken', 'granted', 'despite', 'general', 'awareness'] ['about', 'representations', 'death', 'possibility', 'freely', 'reflect'] ['discuss', 'these', 'issues', 'offered', 'course', 'rendered'] ['therefore', 'significant', 'support', 'participants', 'during'] ['confusing', 'highly', 'dramatic', 'period', 'pandemic'] ['related', 'research', 'results', 'showed', 'course'] ['helped', 'participants', 'consider', 'religious', 'spiritual', 'aspect'] ['corresponding', 'distal', 'defenses', 'indicated'] ['indeed', 'perspective', 'religious', 'spiritual'] ['vision', 'mitigates', 'concerns', 'arise', 'awareness'] ['death', 'allows', 'people', 'focus', 'their', 'attention', 'everyday'] ['without', 'being', 'assailed', 'death', 'anxiety', 'another', 'fundamental'] ['element', 'emerged', 'research'] ['rediscovered', 'importance', 'funeral', 'rites', 'maintain', 'sense'] ['community', 'especially', 'while', 'facing', 'extremely', 'painful', 'historical'] ['periods', 'significantly', 'characterized', 'grief', 'study', 'highlighted'] ['indeed', 'concrete', 'address', 'these', 'problems'] ['wider', 'existential', 'issues', 'ability', 'improve'] ['reflection', 'meaning', 'address', 'sense', 'limit'] ['these', 'results', 'confirm', 'again', 'effectiveness', 'reflection'] ['death', 'dying', 'critical', 'periods', 'covid'] ['pandemic', 'because', 'permits', 'awareness', 'unconscious', 'aspects'] ['characterize', 'terror', 'death', 'gives', 'cultural'] ['relative', 'dimensions', 'especially', 'important', 'healthcare'] ['professionals', 'already', 'generally', 'difficult', 'rather'] ['distressing', 'manage', 'patient'] ['proper', 'training', 'these', 'aspects', 'therefore'] ['pandemic', 'period', 'additional', 'psychological'] ['burden', 'dealing', 'higher', 'mortality', 'salience'] ['proper', 'personal', 'professional', 'tools', 'elaborate', 'break'] ['silence', 'taboo', 'generally', 'surrounds', 'experience'] ['death', 'considering', 'covid', 'pandemic'] ['extremely', 'number', 'deaths', 'their', 'massive', 'burden'] ['healthcare', 'professionals', 'actively', 'daily'] ['basis', 'represent', 'significant', 'public', 'health', 'challenge', 'which'] ['attention', 'should', 'given', 'future'] ['given', 'current', 'study', 'findings', 'therefore', 'introduction'] ['increase', 'death', 'education', 'pathways', 'university', 'courses'] ['could', 'great', 'importance', 'especially', 'present', 'situation'] ['frontiers', 'public', 'health', 'frontiersin', 'april', 'volume', 'article', '625756testoni', 'psychology', 'students', 'perceptions', 'covid'] ['covid', 'pandemic', 'could', 'indeed', 'young', 'adults'] ['obtain', 'proper', 'resources', 'personal', 'skills'] ['adequately', 'period', 'intense', 'uncertainty', 'mortality'] ['salience', 'other', 'future', 'challenge', 'there', 'might', 'store'] ['especially', 'healthcare', 'students', 'psychologists', 'example'] ['properly', 'address', 'these', 'themes'] ['their', 'patients', 'future', 'proper', 'death', 'education', 'courses'] ['should', 'therefore', 'implemented', 'keeping'] ['allow', 'students', 'openly', 'discuss', 'without', 'prejudices'] ['theme', 'death', 'human', 'vulnerability', 'express', 'related'] ['emotions', 'through', 'could', 'helpful'] ['limits', 'future', 'perspectives'] ['research', 'reports', 'encouraging', 'results', 'demonstrate'] ['positive', 'effects', 'death', 'education', 'however', 'limitations'] ['should', 'taken', 'account', 'including', 'inability', 'generalize'] ['results', 'given', 'specificity', 'conditions', 'concerning'] ['future', 'research', 'should', 'focus', 'significantly', 'broaden', 'number'] ['participants', 'addition', 'longitudinal'] ['study', 'considered', 'approach', 'discussion'] ['existential', 'issues', 'addressed', 'actually', 'changed'] ['stable', 'moreover', 'since', 'research', 'participants'] ['mostly', 'female', 'though', 'psychology', 'students'] ['usually', 'female', 'future', 'research', 'could', 'explore'] ['precisely', 'psychology', 'students', 'point'] ['comparing', 'female', 'students', 'other', 'interesting'] ['aspects', 'could', 'investigated', 'future', 'studies', 'could'] ['perspective', 'medical', 'nursing', 'social', 'services', 'students'] ['being', 'conducted', 'other', 'cultural', 'contexts', 'since'] ['comparing', 'psychology', 'students', 'perspectives', 'point'] ['other', 'healthcare', 'social', 'services', 'professionals', 'could'] ['particularly', 'interesting', 'informative'] ['availability', 'statement'] ['supporting', 'conclusions'] ['article', 'available', 'authors', 'without'] ['undue', 'reservation'] ['ethics', 'statement'] ['studies', 'involving', 'human', 'participants'] ['reviewed', 'approved', 'ethical', 'committee'] ['psychological', 'research', 'university'] ['padua', '57bc2002fdf5cbd4292f5f86aa077f23'] ['patients', 'participants', 'provided', 'their', 'written', 'informed', 'consent'] ['participate', 'study'] ['author', 'contributions'] ['contributed', 'research', 'design', 'project', 'planning'] ['supervision', 'research', 'analysis', 'texts', 'methodology'] ['article', 'writing', 'contributed', 'project', 'planning'] ['analysis', 'texts', 'article', 'writing', 'contributed'] ['interviews', 'analysis', 'texts', 'contributed'] ['supervision', 'cooperation', 'organization', 'teamwork'] ['writing', 'contributed', 'analysis', 'texts', 'cooperation'] ['organization', 'contributed'] ['writing', 'authors', 'contributed', 'article', 'approved'] ['submitted', 'version'] ['acknowledgments'] ['thank', 'davide', 'viezzoli', 'marco', 'antonellini', 'alice', 'culcasi'] ['gianmarco', 'biancalani', 'critical', 'phases'] []
# LSA Model
number_of_topics=1
words=word_count
model=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_psychology =dict(model.show_topic(0, topn=words))##dict and encoding matrix values
dict(model.show_topic(0, topn=5))
{'covid': 0.5542434908010379,
'psycholog': 0.38583919707621755,
'pandem': 0.3642661434196234,
'health': 0.29879966770716154,
'impact': 0.15511343595226448}
from wordcloud import WordCloud
text = dict(model.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph26.png')
# LSA Model
number_of_topics = 100 #
words = word_count
model = create_gensim_lsa_model(clean_text,number_of_topics,words)
a=pd.DataFrame()
for i in range(number_of_topics):
words_psy =dict(model.show_topic(i, topn=words))
b=pd.DataFrame(words_psy,index=[i])
a=a.append(b)
a=a.transpose()
top_words = dict(model.show_topic(0,topn=5))
df = pd.DataFrame(columns=list(top_words.keys()),
index = list(top_words.keys()))
l = list(top_words.keys())
print('cosine-similarity')
for i in l:
for j in l:
matrix=np.array([a.loc[i],a.loc[j]])
n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
df.loc[[i],[j]] = n[0,1]
print(df)
dfi.export(df, 'df_styled29.png')
cosine-similarity
covid psycholog pandem health impact
covid 1 -0.000282125 0.000628955 -0.00143385 0.00319398
psycholog -0.000282125 1 -0.00140862 -0.000811757 0.00291916
pandem 0.000628955 -0.00140862 1 -0.000739274 0.000756308
health -0.00143385 -0.000811757 -0.000739274 1 -0.00148988
impact 0.00319398 0.00291916 0.000756308 -0.00148988 1
df = df.apply(pd.to_numeric, errors='coerce')
df=df.round(4)
l = list(top_words.keys())
df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.5)}
a=list(G.edges(data=True))
b=[]
for i in a:
b.append(i[:][2]['weight'])
w = [x *100 for x in b]
colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='g',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig29.png')